• Journal of Korean Academy of Nursing
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• v.25 no.1
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• pp.45-60
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• 1995

This study investigated the effects of education program conducted through individual home visit by CHPs, which was developed ,by the operational re-search technique for families of dementia patients. (Yeo Shin Hong et at,1994) The study was conducted in the form of a primary experimental design with 43 people as subjects, including dementia patients and family member in several Myon areas of Chungcheong Namdo between June 10 and August 20, 1994. The data was collected by questionnaires through the home visit by the CHPs. The results of study are as follows. 1. There is no difference in the quality of life between before and after the education program. 2. Role stress 'before the education program' was significantly different than 'after the education program'. 3. There was no difference in the feeling of burden between before and after the education program. 4. There was a significant difference in the abnormal behaviors of patients between before and after the education program. 5. The knowledge of dementia by the patient's family increased significantly after the education program, compared to that of 'before the education program'. 6. There was a significant difference in the attitude of family members toward the education program on dementia between before and after the education program. 7. The results of analysis on the coefficient relationship of various variables showed that the age of patients and family members have a significant correlation with role stress(p=.01). 8. In the subjective evaluation of family members on changes in actual nursing actions and the improvement of knowledge and technique in terms of daily living, (including abnormal behavior of patients, adjustment of environment for patients, activity programs for patients, communication technique with patients, ensuring the safety of patients, clothing, meals and elimination, 60-65% of family members responded that their knowledge had increased. As for improvement in techniques for each item, the technique for communication with patients showed the greatest improvement while the action program method for patients showed the least change. As for the nursing service provided to patients, most respondents showed a positive change. The specific items for which more than 80% respondents answered positively were as follows : recognizing the demand of patients, getting patients to do simple house works, talking softly and gently, removing dangerous things, preparing comfortable clothes that are easy to put on and take off, and limiting water consumption at night. As a result of study, the following suggestions can be made. The purpose of the study was to examine the effect of an education program developed and applied for dementia patients and family members in the community. This needs to be compared with a similar study conducted in the urban setting. In addition, a community service program (ex : nursing hem and shelter) including the application of the education program should be developed and the study done to investigate its effect.

• Journal of Trauma and Injury
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• v.20 no.2
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• pp.77-82
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• 2007

Purposes: There have been many efforts to improve the service of emergency centers. In spite of these, no evidence is showing any landmark advancement of emergency services, especially in the hospital stage, exists. We need some efficient standard criteria to evaluate emergency service in the hospital stage, and a useful method might utilize the overcrowding index. We want to know the change in the overcrowding index at a regional emergency center after injury area administration. Injury area means an area in which only an assigned duty physician manages patients with injuries such as those from traffic accidents, falls, assualts, collisions, lacerations, amputations, bums, intoxication, asphyxia, drowning, animal bites, sexual assualts, etc. Methods: We started to operate an injury area in our emergency department from late 2004, and from January to June in 2004 and in 2005, we collected patients' data, age, sex, assigned department, and result from hospital order communication system to figure out overcrowding indices and result indices. We found the daily number of patients, the turnover rate, the admission rate, the ICU admission rate, the emergency operation rate, the ED stay duration, and the ED patient volume to be overcrowding indices. Also we found the withdrawal rate, the transfer rate, and mortality to be result indices. We compared these indices between 2004 to 2005 by using a t-test. Results: There was a significant increase in the daily number of visiting patients in 2005, overcrowding indices, such as the turnover rate, the admission rate, the ICU admission rate, and the emergency operation rate, also showed statistically significant increases in 2005 (P<0.001). As for the result indices, there was a noticeable decrease in the number of withdrawals (11.77/day in 2004 to 4.53/day in 2005). Conclusion: Operating an injury area in a mildly overcrowded local emergency center is beneficial. Evaluating the effect of operating an injury area and it's impact on hospital finances by conducting a similar study analyziing patients for a longer duration would be valuable.

• Journal of Korean society of Dental Hygiene
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• v.8 no.3
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• pp.1-11
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• 2008

Oral health projects that cater to the disabled should be more prevailing in order to ensure the maintenance and successful promotion of the oral health of disabled people. 70 public dental clinics that conducted oral health projects geared toward the disabled were examined to get a precise grip on their oral health projects. The findings of the study were as follows: 1. 31 out of 70 public dental clinics investigated(44.3%) were equipped with two or more dental hygienists who were professional human resources in charge of the oral health projects for the disabled. As for the age and disability type of the beneficiaries of the oral health projects, adolescents(74.3%) and people with mental retardation(87.1%) benefited most from the oral health projects. Concerning the most common implementation frequency of the projects, the projects were carried out once to three times a week(62.9%). 2. The most dominant oral disease treatment provided to disabled people was amalgam treatment and resin treatment(68.6%), which were the early dental caries treatment. The most common preventive treatment that was offered to improve their oral health was oral prophylaxis(82.9%). As for reform measures for the oral health projects, education of personnels in charge of the projects and their specialization(58.6%) were most emphasized. 3. Regarding factors related to the preventive oral health projects for the disabled, the implementation of oral prophylaxis and toothbrushing education was linked to the age of the beneficiaries. More oral prophylaxis was offered to teens, and more toothbrushing education was provided to preschoolers and adolescents. The age of the beneficiaries and the number of dental hygienists responsible for the projects had something to do with the application of fluorides. 4. Concerning the relationship of the preventive oral health projects for the disabled to the number of dental hygienists, one of the personnels in charge of the projects, the application of fluorides( 54.4%) and pit & fissure sealing(56.8%) were more prevalent when there were two or more dental hygienists. There was a statistically significant disparity in that regard(p<0.05). The above-mentioned findings illustrated that in order to boost the oral health of the disabled, dental hygienists who are responsible for the oral health projects for the disabled should put ceaseless efforts into fostering their professional knowledge and ability and offering quality service to disabled patients. Every public dental clinic should be equipped with plenty of professional personnels to enlarge the scope of treatment and ensure the efficiency of treatment and the preventive projects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.140-147
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• 2004

Agrometeorological information is essential in many agricultural decisions if it reaches the user in a timely and appropriate manner. Agriculture is the backbone to local, regional, and global economic development. Thus, strengthening agrometeorological application to diverse agricultural sectors will benefit economic development. This paper discusses three distinct organizational minions that all share the same need for improved information technology. The World Meteorological Organization's (WMOs) Commission for Agricultural Meteorology (CAgM) has global responsibility for improved agrometeorological services of Members to aid agricultural production and to conserve natural resources. The United States Department of Agriculture, World Agricultural Outlook Board, publishes monthly World Agricultural Supply and Demand Estimates, considered to be a benchmark for both government and industry in production and trade decisions. The National Drought Policy Commission (NDPC), created by an act of the United States Congress, formulated a national drought policy based on preparedness rather than on crisis management. All three organizations recognize the need for IT applications in agricultural meteorology and have been active in implementing this technology. The development of information technology offers new means of dissemination of agrometeorological products. World Agrometeorological Information Service (WAMIS) has taken advantage of the global Internet application to offer WMO Members a dedicated web server to host agrometeorological bulletins and training modules.

• The Korean Journal of Mycology
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• v.42 no.3
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• pp.225-230
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• 2014

Sclerotinia sclerotiorum, a plant pathogenic fungus, can cause serious yield and quality losses in the winter lettuce field. For biological control of S. sclerotiorum, soil-born microorganisms that inhibit the mycelia growth of S. sclerotiorum and Fusarium oxysporum were isolated from diseased soil. Among the isolates, bacterial isolate, GG95, which was identified as Bacillus subtilis according to the morphological, physiological characteristics and by 16S rRNA similarity, showed the highest level of inhibitory activity. The growth conditions for B. subtilis GG95 were optimized in TSB media (pH 7) by culturing at $28^{\circ}C$ for 24 hrs. Maltose or fructose and peptone were selected as the best carbon and nitrogen sources, respectively. Greenhouse experiment was performed to test effectiveness of B. subtilis GG95 in the control sclerotinia rot. Drench application ($1{\times}10^8cfu/mL$, 3 times) of the bacterial culture broth to lettuce showed an effectiveness value of 88%, suggesting that B. subtilis GG95 would be a promising biocontrol agent for control of sclerotinia rot.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.18-41
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• 2010

The purpose of this paper is to show the empirical measurement way for predicting the seaport efficiency by using Super SBM(Slack-based Measure) with Wilcoxson signed-rank test under CRS(constant returns to scale) condition for 20 Korean ports during 11 years(1997-2007) for 3 inputs(port investment amount, birthing capacity, and cargo handling capacity) and 5 outputs(Export and Import Quantity, Number of Ship Calls, Port Revenue, Customer Satisfaction Point for Port Service and Container Cargo Throughput). The main empirical results of this paper are as follows. First, Super SBM model has well reflected the real data according to the Wilcoxon signed rank test, because p values have exceeded the significance level. Second,Super-SBM has shown about 87% of predicting ratio for the ports efficiency and the optimal size of investment in domestic seaport. The policy implication to the Korean seaports and planner is that Korean seaports should introduce the new methods like Super-SBM method with Wilcoxon signed rank test for predicting the efficiency of port performance and the optimal size of investment as indicated by Panayides et al.(2009, pp.203-204).

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.5
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• pp.729-733
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• 2005

This study was conducted to sanitize fried-frozen cheese ball by gamma irradiation. Total aerobic bacteria and yeasts and molds counts were 4.4 and 2.8 log CFU/g in non-irradiated sample, respectively. Microorganisms were decreased with increase of irradiation dose $(D_{10}=1.25\;kGy)$, and were not detected in samples irradiated at 3 kGy or more $(<10^2\;CFU/g)$. $D_{10}$ value for Escherichia coli (KCTC 1682) was 0.25 kGy. TBA (2-thiobarbituric acid) values were increased as irradiation dose was increased, but there was no significant difference between non-irradiated and irradiated samples at 3 kGy or less (p<0.05). The results of sensory evaluation showed that there was no significant difference between non-irradiated and irradiated samples at 3 kGy or less, the sensory scores were decreased with irradiation at 5 kGy or more (p<0.05). These results indicated that gamma irradiation at 3 kGy was considered to be an effective treatment to ensure the microbiological safety of fried-frozen cheese balls without any sensorial change, even though further studies should be investigated to reduce detrimental effects induced by irradiation.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.