• 한국유가공학회:학술대회논문집
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• 2007.09a
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• pp.49-58
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• 2007

Inflammatory process leads to the well-known mucosal damage and therefore a further disturbance of the epithelial barrier function, resulting abnormal intestinal wall function, even further accelerating the inflammatory process[1]. Despite of the records, etiology and pathogenesis of IBD remain rather unclear. There are many studies over the past couple of years have led to great advanced in understanding the inflammatory bowel disease(IBD) and their underlying pathophysiologic mechanisms. From the current understanding, it is likely that chronic inflammation in IBD is due to aggressive cellular immune responses including increased serum concentrations of different cytokines. Therefore, targeted molecules can be specifically eliminated in their expression directly on the transcriptional level. Interesting therapeutic trials are expected against adhesion molecules and pro-inflammatory cytokines such as TNF-${\alpha}$. The future development of immune therapies in IBD therefore holds great promises for better treatment modalities of IBD but will also open important new insights into a further understanding of inflammation pathophysiology. Treatment of cytokine inhibitors such as Immunex(Enbrel) and J&J/Centocor(Remicade) which are mouse-derived monoclonal antibodies have been shown in several studies to modulate the symptoms of patients, however, theses TNF inhibitors also have an adverse effect immune-related problems and also are costly and must be administered by injection. Because of the eventual development of unwanted side effects, these two products are used in only a select patient population. The present study was performed to elucidate the ability of TNF-${\alpha}$ antibodies produced in sheep colostrums to neutralize TNF-${\alpha}$ action in a cell-based bioassay and in a small animal model of intestinal inflammation. In vitro study, inhibitory effect of anti-TNF-${\alpha}$ antibody from the sheep was determined by cell bioassay. The antibody from the sheep at 1 in 10,000 dilution was able to completely inhibit TNF-${\alpha}$ activity in the cell bioassay. The antibodies from the same sheep, but different milkings, exhibited some variability in inhibition of TNF-${\alpha}$ activity, but were all greater than the control sample. In vivo study, the degree of inflammation was severe to experiment, despite of the initial pilot trial, main trial 1 was unable to figure out of any effect of antibody to reduce the impact of PAF and LPS. Main rat trial 2 resulted no significant symptoms like characteristic acute diarrhea and weight loss of colitis. This study suggested that colostrums from sheep immunized against TNF-${\alpha}$ significantly inhibited TNF-${\alpha}$ bioactivity in the cell based assay. And the higher than anticipated variability in the two animal models precluded assessment of the ability of antibody to prevent TNF-${\alpha}$ induced intestinal damage in the intact animal. Further study will require to find out an alternative animal model, which is more acceptable to test anti-TNF-${\alpha}$ IgA therapy for reducing the impact of inflammation on gut dysfunction. And subsequent pre-clinical and clinical testing also need generation of more antibody as current supplies are low.

• Tuberculosis and Respiratory Diseases
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• v.43 no.5
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• pp.701-708
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• 1996

Background : To evaluate the effect of the balloon dilatation in tuberculous bronchial stenosis, we performed balloon dilatation in 13 cases which had airway obstruction in main bronchus with the impairment of pulmonary function. Material and Methods: Thirteen women with tuberculous bronchial stenosis(9cases : left main bronchus, 4 cases: right main bronchus) underwent fluoroscopically guided balloon dilatation under the local anesthesia. Among the these patient, 9 cases were active endobronchial tuberculosis, and 4 cases were inactive. Immediate and long term follow-up(average 15.6months) assessments were done focused on change on PIT. The increase of FVC or FEV1 more than 15% after the procedure was considered effective. Complications after dilatation were evaluated in all patients. Result : 1) There were an decrease of self-audible wheezing in 75%(6/8), improvement of dyspnea in 62.5%(5/8), improvement of cough and expectoration in 50%(3/6), and improvement of chest discomfort in 50%(1/2). 2) Significant improvement of PFT was noted in 42.9%(3/7) of which respiratory symptoms duration was below 6 months. 8m, significant improvement of PFT was noted in only 25%(1/4) of which respiratory symptoms duration was above 12 months. 3) Active stage was 69.2%(9/13) and inactive was 30.8%(4/13). There was an significant improvement of PFT in 44.4%(4/9) of active stage, but, only 25%(1/4) of inactive stage was improved. 4) In 61.5%(8/13), FVC and FEV1 were increased to 35.5%, and 22.2% at post-dilatation 7 days. After 1 month later, FVC and FEV1 were increased to 54.7%, and 31.8% in 5 cases(38.5%). 4 cases in which long-term follow-up(average 19.8months) was possible the improvement of FVC, and FEV1 were 30.5%, and 10.1%. 5) Just after balloon dilatation therapy, transient leukocytosis or fever was noted in 30.8%(4/13), and blood-tinged sputum was noted in 30.8%(4/13). However, serious complication, such as pneumothorax, pneumomediastinum or mediastinitis, was not noted. Conclusion : We conclude that tuberculous bronchial stenosis, which is on active stage, and short dulation of respiratory symptoms was more effective on balloon dilatation than inactive stage or long duration of respiratory symptoms. Furthermore, balloon dilatation is easier, much less invasive and expensive than open surgery. and cryotherapy or photoresection. Because of these advantage, we think that balloon dilatation could be the first choice for treating bronchial stenosis and could be done at first in early stage if unresponsiveness with steroid therapy is observed.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• The Korean Journal of Rehabilitation Nursing
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• v.2 no.2
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• pp.257-270
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• 1999

The purpose of this study is to investigate factors related to the intent of using home nursing of chronic disease patients who got out of a university hospital. For the purpose, the study selected 153 patients who were hospitalized and left K university hospital with diagnoses of cancer, hypertension, diabetes and cerebral vascular accident and ordered to be discharged and performed interviews with them and surveys on their medical records to obtain the following results. For this study a direct-interview survey and medical record review was conducted from June 28 to Aug. 30, 1998. The frequency and mean values were computed to find the characteristics of the study subjects, and $X^2$-test, t-test, factor analysis and multiple logistic regession analysis were applied for the analysis of the data. The following results were obtained. 1) When characteristics of the subjects were examined, men and women occupied for 58.8% and 41.2%, respectively. The subjects were 41.3 years old in aver age and had the monthly aver age earning of 0.99 million won or below, which was the most out of the total subjects at 34.6%. Among the total, 87.6% resided in cities and 12.4 in counties. The most left the hospital with diagnosis of cancer at 51.6%, followed by hyper tension at 24.2%, diabetes at 13.7% and cerebral vascular accident at 7.2%. 2) 93.5% of the selected patients had the intent of using home nursing and 6.5%, didn't. Among those patients having the intent, 85.6% had the intent of paying for home nursing and 14.4%, didn't. The subjects expected that the nursing would be paid 9,143 won in aver age and 47.7% of them preferred national authorities as the main servers. 86.3% of the subjects thought that home nursing business had the main advantage of making it possible to learn nursing methods at home and thereby contributing to improving the ability of patients and their facilities to solve health problems. 3) Relations between the intent of use and characteristics of the subjects such as demography-related social, home environment, disease and physical function characteristics did not show statistically significant differences among one another. Compared to those who had no intent of using home nursing, the group having the intent had more cases of male patients, the age of 39 or below, residence in cities, 5 family member s or more, no existence of home nursing servers, leaving the hospital from a non-hospitalized building, disease development for five months or below, hospitalization for ten days or more, non-hospitalization with in the recent one month, two times or over of hospitalization, leaving the hospital with no demand of special treatment, operation underwent, poor results of treatment, leaving the hospital with demand of rehabilitation services, physical disablement and high evaluation point of daily life. 4) Among those patients having the intent of using home nursing, 47.6% demanded technical nursing and 55.9%, supportive nursing. As technical nursing,' inject into a blood vessel ' and 'treat pustule and teach basic prevention methods occupied for 57.4%, respectively, topping the list. Among demands of supportive nursing, 'observe patients 'status and refer them to hospitals or community resources as available, if necessary' was the most with percent age point of 59.5. Regarding the intent of paying for home nursing, 39.2% of those patients wishing to use the nursing responded paying for technical services and 20.2, supportive services. In detail, 70.0% wanted to pay for a service stated as 'inject into a blood vessel', highest among the former services and 30.7%, a service referred to as 'teaching exercises needed to make the body of patients move', highest among the latter. When this was analyzed in terms of a relation between the need(the need for home nursing) and the demand(the intent of paying for home nursing), The rate of the need to the demand was found two or three times higher in technical nursing(0.82) than in supportive nursing(0.35). In aspects of tech ical nursing, muscle injection(1.26, the 1st rank) was highest in the rate while among aspects of supportive nursing, a service referred to as 'teach exercises needed for making patients move their bodies normally'(0.58, the 1st rank). 5) factors I(satisfaction with hospital services), II(recognition of disease state), III(economy) and IV(period of disease) occupied for 34.4, 13.8, 11.9 and 9.2 percents, respectively among factors related to the intent by the subjects of using home nursing, totaled 59.3%. In conclusion, most of chronic disease patients have the intent of using hospital-based home nursing and satisfaction with hospital services is a factor affecting the intent most. Thus a post-management system is needed to continue providing health management to those patients after they leave the hospital. Further, supportive services should be provided in order that those who are satisfied with hospital services return to their community and live their in dependent lives. Based on these results, the researcher would make the following recommendation. 1) Because home nursing becomes more and more needed due to a sharp increase in chronic disease patients and elderly people, related rules and regulations should be made and implemented. 2) Hospital nurses specializing in home nursing should be cultivated.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.122-127
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• 2010

Purpose: The Xpress3.$cardiac^{TM}$ which is a kind of wide beam reconstruction (WBR) method developed by UltraSPECT (Haifa, Israel) enables the acquisition of at quarter time while maintaining image quality. The purpose of this study is to investigate the usefulness of WBR method for decreasing scan times and to compare to it with filtered back projection (FBP), which is the method routinely used. Materials and Methods: Phantom and clinical studies were performed. The anthropomorphic torso phantom was made on an equality with counts from patient's body. The Tl-201 concentrations in the compartments were 74 kBq (2 ${\mu}Ci$)/cc in myocardium, 11.1 kBq (0.3 ${\mu}Ci$)/cc in soft tissue, and 2.59 kBq (0.07 ${\mu}Ci$)/cc in lung. The non-gated Tl-201 myocardial perfusion SPECT data were acquired with the phantom. The former study was scanned for 50 seconds per frame with FBP method, and the latter study was acquired for 13 seconds per frame with WBR method. Using the Xeleris ver. 2.0551, full width at half maximum (FWHM) and average image contrast were compared. In clinical studies, we analyzed the 30 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Medical Center from January to April 2010. The patients were imaged at full time (50 second per frame) with FBP algorithm and again quarter-time (13 second per frame) with the WBR algorithm. Using the 4D MSPECT (4DM), Quantitative Perfusion SPECT (QPS), and Quantitative Gated SPECT (QGS) software, the summed stress score (SSS), summed rest score (SRS), summed difference score, end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) were analyzed for their correlations and statistical comparison by paired t-test. Results: As a result of the phantom study, the WBR method improved FWHM more than about 30% compared with FBP method (WBR data 5.47 mm, FBP data 7.07 mm). And the WBR method's average image contrast was also higher than FBP method's. However, in result of quantitative indices, SSS, SDS, SRS, EDV, ESV, EF, there were statistically significant differences from WBR and FBP(p<0.01). In the correlation of SSS, SDS, SRS, there were significant differences for WBR and FBP (0.18, 0.34, 0.08). But EDV, ESV, EF showed good correlation with WBR and FBP (0.88, 0.89, 0.71). Conclusion: From phantom study results, we confirmed that the WBR method reduces an acquisition time while improving an image quality compared with FBP method. However, we should consider significant differences in quantitative indices. And it needs to take an evaluation test to apply clinical study to find a cause of differences out between phantom and clinical results.

• Journal of agricultural medicine and community health
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• v.27 no.1
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• pp.99-113
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• 2002

For this study a sample of 205 people, 66 males and 139 females, over 65 years of age, residing in C-gu of S-si and utilizing senior centers, were selected, The objective of the study was to provide basic data for health promotion program development provided by health centers. A questionnaire was used to collect date on general characteristics, health status, social health status and utilization rate for health services. The instruments used in this study were the Lawton scale, to measure daily routine function, the MMSE-K developed by Folstein and modified to fit the Korea situation, for mental health status, and the CES-Dtool developed by Radloff, for emotional health status. the SPSS Window program was used to calculate percentages. Tests of significance were done using t-test and ANOVA. Multiple regression analysis was used to identify variables influencing the use of health services. The results are as follows : Of those utilizing senior citizen centers, 40.9% of males and 17.3% of the female thought they were healthy. The average score for IADL was 7.4. The daily routine of female respondents consisted of buying household articles and drugs, and other IADLs such as riding the bus or subway alone. These resulted in a higher score compared to males. For emotional health, 7.6% of the males reported depression compared to 21.6% of the females. For mental health, 48.5% of the males and 28.8% of the females were found to be in the group suspicious for dementia. On social health, 57.6% of the males and 62.6% of the females reported no intimate human relations. Of those older people who had close human relations, 52.5% of the males indicated a friend as the closest person and 53.8% of the females, their children. On use of health services, there was a significantly higher need for mobile medical care services treatment for those with lower education levels and status of window/widower. There was a significantly higher need for health exmination services for those with lower levels of exercise, greater satisfaction with sleep, higher levels of oral health care, and higher social contacts. In conclusion, there is a need to provide varied programs for the promotion of health, along with parallel resolution of social, psychological and economic issues. It is recommended that health services for elderly people provided by the health centers be implemented with full recognition of these characteristics and differences.

• Journal of Hospice and Palliative Care
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• v.5 no.2
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• pp.111-124
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• 2002

Purpose : Although the average life expectancy has increased due to advances in medicine, mortality due to cancer is on an increasing trend. Consequently, the number of terminally ill cancer patients is also on the rise. Predicting the survival period is an important issue in the treatment of terminally ill cancer patients since the choice of treatment would vary significantly by the patents, their families, and physicians according to the expected survival. Therefore, we investigated the prognostic factors for increased mortality risk in terminally ill cancer patients to help treat these patients by predicting the survival period. Methods : We investigated 31 clinical parameters in 157 terminally ill cancer patients admitted to in the Department of Family Medicine, National Health Insurance Corporation Ilsan Hospital between July 1, 2000 and August 31, 2001. We confirmed the patients' survival as of October 31, 2001 based on medical records and personal data. The survival rates and median survival times were estimated by the Kaplan-Meier method and Log-rank test was used to compare the differences between the survival rates according to each clinical parameter. Cox's proportional hazard model was used to determine the most predictive subset from the prognostic factors among many clinical parameters which affect the risk of death. We predicted the mean, median, the first quartile value and third quartile value of the expected lifetimes by Weibull proportional hazard regression model. Results : Out of 157 patients, 79 were male (50.3%). The mean age was $65.1{\pm}13.0$ years in males and was $64.3{\pm}13.7$ years in females. The most prevalent cancer was gastric cancer (36 patients, 22.9%), followed by lung cancer (27, 17.2%), and cervical cancer (20, 12.7%). The survival time decreased with to the following factors; mental change, anorexia, hypotension, poor performance status, leukocytosis, neutrophilia, elevated serum creatinine level, hypoalbuminemia, hyperbilirubinemia, elevated SGPT, prolonged prothrombin time (PT), prolonged activated partial thromboplastin time (aPTT), hyponatremia, and hyperkalemia. Among these factors, poor performance status, neutrophilia, prolonged PT and aPTT were significant prognostic factors of death risk in these patients according to the results of Cox's proportional hazard model. We predicted that the median life expectancy was 3.0 days when all of the above 4 factors were present, $5.7{\sim}8.2$ days when 3 of these 4 factors were present, $11.4{\sim}20.0$ days when 2 of the 4 were present, and $27.9{\sim}40.0$ when 1 of the 4 was present, and 77 days when none of these 4 factors were present. Conclusions : In terminally ill cancer patients, we found that the prognostic factors related to reduced survival time were poor performance status, neutrophilia, prolonged PT and prolonged am. The four prognostic factors enabled the prediction of life expectancy in terminally ill cancer patients.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Tuberculosis and Respiratory Diseases
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• v.64 no.6
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• pp.433-438
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• 2008

Background: Measurement of the maximum oxygen uptake in patients with chronic obstructive pulmonary disease (COPD) has been used to determine the intensity of exercise and to estimate the patient's response to treatment during pulmonary rehabilitation. However, cardiopulmonary exercise testing is not widely available in Korea. The 6-minute walk test (6MWT) is a simple method of measuring the exercise capacity of a patient. It also provides high reliability data and it reflects the fluctuation in one' s exercise capacity relatively well with using the standardized protocol. The prime objective of the present study is to develop a regression equation for estimating the peak oxygen uptake ($VO_2$) for men with moderate to very severe COPD from the results of a 6MWT. Methods: A total of 33 male patients with moderate to very severe COPD agreed to participate in this study. Pulmonary function testing, cardiopulmonary exercise testing and a 6MWT were performed on their first visits. The index of work ($6M_{work}$, 6-minute walk distance [6MWD]${\times}$body weight) was calculated for each patient. Those variables that were closely related to the peak $VO_2$ were identified through correlation analysis. With including such variables, the equation to predict the peak $VO_2$ was generated by the multiple linear regression method. Results: The peak $VO_2$ averaged $1,015{\pm}392ml/min$, and the mean 6MWD was $516{\pm}195$ meters. The $6M_{work}$ (r=.597) was better correlated to the peak $VO_2$ than the 6MWD (r=.415). The other variables highly correlated with the peak $VO_2$ were the $FEV_1$ (r=.742), DLco (r=.734) and FVC (r=.679). The derived prediction equation was $VO_2$ (ml/min)=($274.306{\times}FEV_1$)+($36.242{\times}DLco$)+($0.007{\times}6M_{work}$)-84.867. Conclusion: Under the circumstances when measurement of the peak $VO_2$ is not possible, we consider the 6MWT to be a simple alternative to measuring the peak $VO_2$. Of course, it is necessary to perform a trial on much larger scale to validate our prediction equation.