• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of the Korean Wood Science and Technology
• /
• v.6 no.2
• /
• pp.3-19
• /
• 1978

Five species, Abies koreana Wilson (A. koreana), Castanopsis cuspidata var. Sieboldii Nakai (C. Cuspidata). Machilus thunbergii Sieb. et Zucc. (M. thunbergii), Styrax japonica (S. japonica), and Quercus acuta Thunberg(Q. acuta) growing in the southern part of Korea were selected for the investigation of wood properties. In order to evaluate the wood properties of these five species, anatomical, physical, mechanical, chemical and pulping characteristics were investigated. And this study also covered wood technological problems related to the drying, gluing, debarking, flooring, and wood workability so that these species might serve to the best advantage. The results obtained were summarized as follows: 1. The trunk of A. koreana with many knots was straight. However, the trunks of S. japonica and C. cuspidata were crooked. 2. A. koreana showed the longest and the widest ill the fiber morphology; 2.97mm in length, 39.3${\mu}$ in width. In general, fiber width of all the species investigated were greater than those of other Korean hardwoods. 3. The specific gravity of Q. acuta was 0.74${\pm}$0.03, and that of A.koreana was 0.34${\pm}$0.02. The range of specific gravity of the other species was 0.47-0.52. 4. The adsorption of water was propotioned inversely with the specific gravity, but the adsorption of humidity was proportioned with the specific gravity. In spite of their medium density, S. japonica showed the greatest adsorption, and M. thunbergii the least. The water adsorption of cross section was twice greater than that of lateral direction, and there was a slight difference in between the radial and the tangential direction. 5. Shrinkage for tested five species was ranged from 5.36 to 10.24% in tangential direction, and 2.83~6.13% in radial direction. Q. acuta recorded the greatest shrinkage rate, and A. koreana the least. The greater was the specific gravity, the larger was the shrinkage rate. 6. The mechanical properties of Q. acuta were similar to those of Quercus mongolica which grow in Kangwon-Do. Strength properties of C. cuspidata, M. thunbergii, A. koreana were equivalent to those of other Korean commercial woods with similar specific gravity, except S. japonica which showed slightly higher strength than that of other species with similar density. 7. Higher glue joint strength for urea and phenol adhesieves was recorded in the species of M. thunbergii and C. cuspidata, however, high-density species(Q. acuta) and even low-density species(A. koreana) did not show good joint strength. 8. The attractive figure of M. thunbergii in texture seemed to he appreciated for decoration. And the grain and texture of other species were proper for furniture and building materials. 9. All of the species except Q. acuta were considered good for wood workability. 10. The denser the specific gravity was, the longer the drying time took. However, severe drying defects were formed in M. thunbergii whose density was medium. 11. All the species were considered suitable for the flooring wood expect A. koreana whose density was light. 12. Pentosan component in all the species was great, and the amount of extractives in Q. acuta was worth noticing. 13. Yield in kraft pulp was above the level of economic pulp yield, i.e. 45% in all species. 14. Debarking was easy in the species of A. koreana and M. thunbergii, and debarking after being boiled in water was the most efficient in all species.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.111-131
• /
• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.97-117
• /
• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.111-126
• /
• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of the Korean Society of Radiology
• /
• v.11 no.1
• /
• pp.27-35
• /
• 2017

The purpose of this study was to investigate the features of CsI:Tl and $Gd_2O_2S$ detectors with an indirect conversion method using phantom in the DR (digital radiography) system by obtaining images of thick chest phantom, medium thickness thigh phantom, and thin hand phantom and by analyzing the SNR and CNR. As a result of measuring the SNR and CNR according to the thickness change of the subject, the SNR and CNR were higher in CsI:Tl detector than in $Gd_2O_2S$ detector when the medium thickness thigh phantom and thin hand phantom were scanned. However, when the thick chest phantom was used, for the SNR at 80~125 kVp and the CNR at 80~110 kVp in the $Gd_2O_2S$ detector, the values were higher than those of CsI:Tl detector. The SNR and CNR both increased as the tube voltage increased. The SNR and CNR of CsI:Tl detector in the medium thickness thigh phantom increased at 40~50 kVp and decreased as the tube voltage increased. The SNR and CNR of $Gd_2O_2S$ detector increased at 40~60 kVp and decreased as the tube voltage increased. The SNR and CNR of CsI:Tl detctor in the thin hand phantom decreased at the low tube voltage and increased as the tube voltage increased, but they decreased again at 100~110 kVp, while the SNR and CNR of $Gd_2O_2S$ detector were found to decrease as the tube voltage increased. The MTF of CsI:Tl detector was 6.02~90.90% higher than that of $Gd_2O_2S$ detector at 0.5~3 lp/mm. The DQE of CsI:Tl detector was 66.67~233.33% higher than that of $Gd_2O_2S$ detector. In conclusion, although the values of CsI:Tl detector were higher than those of $Gd_2O_2S$ detector in the comparison of MTF and DQE, the cheaper $Gd_2O_2S$ detector had higher SNR and CNR than the expensive CsI:Tl detector at a certain tube voltage range in the thick check phantom. At chest X-ray, if the $Gd_2O_2S$ detector is used rather than the CsI:Tl detector, chest images with excellent quality can be obtained, which will be useful for examination. Moreover, price/performance should be considered when determining the detector type from the viewpoint of the user.

• The Korean Journal of Nuclear Medicine
• /
• v.38 no.4
• /
• pp.325-329
• /
• 2004

Purpose: Usefulness of mouse liver S9 fraction was evaluated for the measurement of the metabolites in the in vitro metabolism study of $^{18}F$-labeled radiotracers. Materials and Methods: Mouse liver S9 fraction was isolated at au early step in the course of microsome preparation. The in vitro metabolism studies were tarried out by incubating a mixture containing the radiotracer, S9 fraction and NADPH at $37^{\ciirc}C$, and an aliquot of the mixture was analyzed at the indicated time points by radio-TLC. Metabolic defluorination was further confirmed by the incubation with calcium phosphate, a bone mimic. Results: The radiotracer $[^{18}F]1$ underwent metabolic defluorination within 15 min, which was consistent with the results of the in vivo method and the in vitro method using microsome. Radiotracer $[^{18}F]2$ was metabolized to three metabolites including $4-[^{18}F]fluorobenzoic$ acid within 60 min. It is likely that the one of these metabolites at the origin of radio-TLC was identical with the one that obtained from the in vivo and in vitro (microsome) method. Compared with the in vitro method using microsome, the method using S9 fraction gave a similar pattern of the metabolites but with a different ratio, which can be explained by the presence of cytosol in the S9 fraction. Conclusion: These results suggest that the findings of the in vitro metabolism studies using S9 fraction can reflect the in vivo metabolism of novel radiotracers in the liver. Moreover, this method can be used as a tool to determine metabolic defluorination along with calcium phosphate absorption method.

• Journal of Preventive Medicine and Public Health
• /
• v.27 no.2 s.46
• /
• pp.258-273
• /
• 1994

To compare the physical complaints of vinylhouse farmers with those of non-vinylhouse farmers, a personal interviews on 250 vinylhouse and 142 non-vinylhouse farmers were conducted in Sungjoo county in Kyungpook province selected by a random sampling from July 5 to July 10, 1993. Blood pressure of the subjects was also measured. Vinylhouse farmers had a higher average age, larger family size, shorter experience of farming, more working hours per day and working days per year and higher annual income than the non-vinylhouse farmers. The frequency of pesticide spray of the vinylhouse farmers was 3.4 times on the average in June 1993 as compared with 2.0 times of non-vinylhouse farmers, and 16.7 times for the vinylhouse farmers during the last one year while it was 8.3 times for the non-vinylhouse farmers in the same period. While 39.6% of vinylhouse farmers experienced pesticide intoxication symptoms such as headache, nausea, vomiting, dizziness, itching, and skin irritation, etc. during the month of June, 25.4% of non-vinylhouse farmers experienced such symptoms. The most frequent symptoms among eight symptoms that constitute the farmers' syndrome were lumbago, numbness of hand or foot, shoulder pain and dizziness regardless of sex and type of farming. Prevalence of the farmers' syndrome in male and female among vinylhouse farmers were 22.1%, 43.4%, respectively, and the prevalence in non-vinylhouse farmers was 23.2% for male and 50.7% for female. There was no statistically significant difference in the prevalence of farmers' syndrome between vinylhouse and non-vinylhouse farmers. However, the prevalence in female was about 2 times higher than that of male. When the effects of other factors were adjusted by multiple logistic regression for farmers' syndrome, the prevalence in female was 3.0 times higher than that of male. The prevalence of farmers' syndrome was increased as the age of farmers increased in both vinylhouse and non-vinylhouse farmers, and adjusted odds ratio of farmers' syndrome increased by 3% as the age increased by 1 year. Adjusted odds ratio for Farmers' syndrome in farmers who experienced pesticide intoxication during the month of June was 3.1 times higher than that of farmers who did not have such experience. While the prevalence of hypertension in male and female non-vinylhouse farmers were 22.4%, 13.7%, respectively, the prevalence in vinylhouse farmers were 13.5% for male and 12.0% for female. However, there was no association between farmers' syndrome and hypertension. It was found in this study that the vinylhouse farmers are at a high risk of pesticide intoxication, which is associated with tile common physical complaints. To reduce such risk it is necessary to develop farming methods which do not require the pesticide or may use less pesticide, a safer method of pesticide spraying, and the protective equipments which can be worn at a high temperature and have a better protective effect. Also education of farmers for the correct methods of ventilation after pesticide spraying in the vinylhouse and wearing the protective equipments may be considered as a supportive method. Since inappropriate posture at work and intensive labor may cause farmers' syndrome, it is recommended to develop farming tools which reduce physical burden and take a rest and exercise periodically during work. It is necessary to strengthen the hypertension management program of the Kyungpook province, because the prevalence of hypertension was as high as about 15%.

• Journal of Biosystems Engineering
• /
• v.6 no.2
• /
• pp.20-32
• /
• 1982

This survey was conducted to investigate the present status of farm tractor utilization for obtaining a basic reference to the establishment of the government's agricultural mechanization strategies. Thirty two counties from the eight provinces except Jeju were covered in this study. From these selected areas, 433 sample farms having farm tractor were taken to obtain the general informations by the enquete, and 93 sample farms among them to investigate the status of daily tractor use in the year of 1980. The analyzed results are summarized as follows: 1. Farm tractors owned by the rice-oriented farms holds 71.5 percent of the total number of tractors the livestock-oriented farms 17.0 percent, and the orchard-oriented farms 7.0 percent. Among the farm tractors 64.3 percent was a large size (46ps) and 35.7 percent a small size(19~23ps). 2. Most of the tractors surveyed were equipped with the essential attachments such as plow and rotavator. About 18 percent of the tractor owners had no trailer, which seemed too high considering the large percentage of tractor use for transportation. The availability of other attachments was very low except a grader on the rice-oriented farms and a hay harvester and a front loader on the livestock-oriented farms. 3. The average size of farm was 3.9 hectare for the rice-oriented farms, 13.9 hectare for the livestock-oriented farms and 7.4 hectare for the orchard-oriented farms. It was obious that the average farm size of was too small compared to the theoretical machine capacity of the tractors. 4. About 70 percent of the tractor operators were in the age of twenties and thirties. About 90 percent of them had an educational level of middle school graduate or above even though their technical level was very low. 5. Any particular problem in tractor use was not found in this survey. From the farmer's preference for purchasing a new tractor, however, it is estimated the demand on a 20-30ps tractor will be more increased. 6. The average annual use of tractor was of about 100 days or 400 hours. It appeared that the rice-oriented farms used most with 412.4 hours per year, and followed by the livestock-oriented farms with 403.6 hours, the orchard oriented farms with 377.7 hours. 7. Among the total hours of tractor use, 47.3 percent was for transportation, and 41.6 percent was for plowing and rotary tillage. The largest portion of the annual tractor use was taken by transportation on the livestock-oriented farms, by land preperation on the rice-oriented farms, and by loading and chemical spraying on the orchard-oriented farms. 8. The hours of tractor use had a peak in May. The hours of use for own farm was remarkably different among the different farm oriented, but there was no considerable difference between the too different sizes of tractor. 9. The hours of tractor use decreased as the age of the operator or the educational level increased. The reason might be that the operators who had a high educational level or were older had a tendency of disliking custom works. 10. The average custom use of tractor was 171.3 hours per year, and the ratio of custom work was 63.7 percent on the rice-oriented farms, 31.7 percent on the livestock-oriented farms and 22.4 percent on the orchard-oriented farms. Among the custom works, the most popular one was the grader leveling. 11. The charge on custom work was about 40,000 Won per hectare for plowing and rotary tillage, and it was the most expensive in the southeastern region, and next followed by the southwestern region. 12. The average plowing capacity of the small tractor was 7.8 hours per hectare in the paddy field, and that of the large tractors was 4.3 hours per hectare. The average rotary-tilling capacities of the small and the large tractors were 6.5 and 4.3 hours per hectare, in the paddy field respectively.