• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.83-96
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• 2016

Through the research and analysis on the vegetation environment, flora of habitats through documentary and field studies over 14 habitats of Euryale ferox Salisbury within Jeollabukdo, with the objective of acquiring the basic data for forming an environment based on plantation of reservoirs that are composed with Euryale ferox, the following results were obtained. 1. The entire flora of the 14 habitats appeared to be 79 families, 211 genus, 298 species, two subspecies, 30 varieties and six forma, thus, a total of 336 taxa was confirmed. Among these, emergent water plants appeared to compose 17 taxa, floating-leaved plants to compose seven taxa including Euryale ferox floating plants to compose five taxa and submerged water plants to compose two taxa. As a result of analyzing the similarity only over the water plants. The lowest similarity rate appeared between Gamdong Reservoir and Aedang Reservoir, as the similarity rate between the two regions appeared to be 0% as a result of the analysis. Floating-leaved plants, lotuses and caltrops, appeared to be equally inhabiting in Hanseongji at Jeongeup and Seoknam Reservoir at Gochang, which showed the highest similarity rate, in addition to Euryale ferox. 2. When examining the appearance frequency of aquatic plants per growth type, Actinostemma lobatum and Phragmites communis, in addition to Euryale ferox each appeared 11 times, showing a high frequency of 78.6% and Trapa japonica, which is a floating-leaved water plant, appeared ten times(71.4%) and Zizania latifolia appeared eight times(57.1%). In addition, the appearance rate appeared to be high in the order of Persicaria thunbergii, Leersia sayanuka, Ceratophyllum demersum, Echinochloa crusgalli var. oryzicola, Scirpus maritimus, and Nelumbo nucifera. 3. The rare plants discovered in the Euryale ferox habitats pursuant to the IUCN evaluation standards was confirmed to be composed of five taxa, with three taxa including the least concerned species(LC), Melothria japonica at Yanggok Reservoir, Hydrocharis dubia at Myeongdeokji and Ottelia alismoides at Daewi Reservoir, in addition to vulnerable species(VU), Utricularia vulgaris at Sangpyeong Reservoir, along with Euryale ferox. 4. Most of the group or community types of the natural habitats of Euryale ferox appeared to be the Euryale ferix community' and the Daewi Reservoir of Gunsan was defined as caltrop + Euryale ferox + Nymphoides indica community. The green coverage ratio of Euryale ferox per natural habitats showed a considerably huge deviation from 0.03 to 36.50 and as the average green coverage ratio was appropriated as 9.8, it can be considered that maintaining the green coverage ratio of Euryale ferox in a 10% level would be advisable when forming a reservoir with Euryale ferox as the key composition species. 5. The vegetation community nearby the natural habitats of Euryale ferox per research subject area appeared to be composed of three Leersia japonica communities, two communities each for Zizania latifolia community and Trapa japonica community and one community each for Nelumbo nucifera community, Nymphoides peltata + Typha orientalis community, Trapa japonica + Nelumbo nucifera community, Hydrocharis dubia community, Leersia japnica + Paspalum distichum var. indutum community and Euryale ferox + Trapa japonica community, showing a slight difference depending on the location conditions of each reservoir. Thus, this result may be suggested as a guideline to apply when allocating the vegetation ratio and the types of floating-leaved plants upon planting plants in reservoirs with Euryale ferox as the main companion species.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.285-301
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• 2020

Looking at the problem of aging in the nation from a demographic perspective, it is not a problem of the overall population, but of the structure of the population. It is the baby boomer and post-baby boomers, the largest population in the country. Baby boomers were born between 1955 and 1963, and currently have a population of 7001,333, which is 13.6 percent (as of 2015). The Post-Baby Boomer generation was born between 1964 and 1974, with a total population of 9,567,171, accounting for 18.8 percent of the total population. In particular, baby boomers and post-baby boomers (32.4% of the total population) have begun to retire or will retire soon. The average life expectancy continues to increase due to the development of medical technology, and the falling birth rate of newborns and the declining population of the production population are darkening the domestic economy. In a policy proposal aimed at easing the nation's falling economic growth rate, women's participation rate is as high as Sweden and men's efforts to increase it as high as Japan's, while the elderly rate is desirable to maintain Korea's high level. This is because the expansion of the elderly generation's participation in economic activities could ease a sharp drop in economic growth and reduce the burden of supporting the elderly population. The study, based on this social problem awareness and problem solving plan, looks at the relationship between career orientation and satisfaction in start-up education based on the diverse career base of active seniors, and also suggests the importance of customized start-up education on the diversity of active seniors by clarifying the relationship between them, and suggests the desirable direction of senior start-up policy design, funding, and start-up education. Based on the theoretical background, the concept of five factors was defined: active senior, career-oriented, satisfaction level of start-up education, willingness to start a business, and the concept definition of an active senior, which is particularly key to the baby boomers in their 50s and 60s, is generally regarded as a source of consumption or welfare benefits, but in this study, the concept of active start-up is reflected in the domestic start-up market by young people in their 40s, 50s and 60s. As a result of a hypothesis test. Hypothesis 1 and Hypothesis 5: Career orientation has been verified to affect the willingness to start a business and the behavior of preparation for a start-up. Hypothesis 3: The willingness to start a business has been verified as having an effect between startup preparation actions. Hypothesis 4: The satisfaction level of start-up education has been verified to affect start-up preparation behavior. However, hypothesis 2: The satisfaction level of education for start-ups does not affect the willingness to start a business. Such results can be inferred that satisfaction in start-up education does not have a direct effect on the will to start a business and increases the will to start a business through the influence of personal career orientation.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.1-11
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• 2008

As conventional oil and gas reservoirs become depleted, interests for oil sands has rapidly increased in the last decade. Oil sands are mixture of bitumen, water, and host sediments of sand and clay. Most oil sand is unconsolidated sand that is held together by bitumen. Bitumen has hydrocarbon in situ viscosity of >10,000 centipoises (cP) at reservoir condition and has API gravity between $8-14^{\circ}$. The largest oil sand deposits are in Alberta and Saskatchewan, Canada. The reverves are approximated at 1.7 trillion barrels of initial oil-in-place and 173 billion barrels of remaining established reserves. Alberta has a number of oil sands deposits which are grouped into three oil sand development areas - the Athabasca, Cold Lake, and Peace River, with the largest current bitumen production from Athabasca. Principal oil sands deposits consist of the McMurray Fm and Wabiskaw Mbr in Athabasca area, the Gething and Bluesky formations in Peace River area, and relatively thin multi-reservoir deposits of McMurray, Clearwater, and Grand Rapid formations in Cold Lake area. The reservoir sediments were deposited in the foreland basin (Western Canada Sedimentary Basin) formed by collision between the Pacific and North America plates and the subsequent thrusting movements in the Mesozoic. The deposits are underlain by basement rocks of Paleozoic carbonates with highly variable topography. The oil sands deposits were formed during the Early Cretaceous transgression which occurred along the Cretaceous Interior Seaway in North America. The oil-sands-hosting McMurray and Wabiskaw deposits in the Athabasca area consist of the lower fluvial and the upper estuarine-offshore sediments, reflecting the broad and overall transgression. The deposits are characterized by facies heterogeneity of channelized reservoir sands and non-reservoir muds. Main reservoir bodies of the McMurray Formation are fluvial and estuarine channel-point bar complexes which are interbedded with fine-grained deposits formed in floodplain, tidal flat, and estuarine bay. The Wabiskaw deposits (basal member of the Clearwater Formation) commonly comprise sheet-shaped offshore muds and sands, but occasionally show deep-incision into the McMurray deposits, forming channelized reservoir sand bodies of oil sands. In Canada, bitumen of oil sands deposits is produced by surface mining or in-situ thermal recovery processes. Bitumen sands recovered by surface mining are changed into synthetic crude oil through extraction and upgrading processes. On the other hand, bitumen produced by in-situ thermal recovery is transported to refinery only through bitumen blending process. The in-situ thermal recovery technology is represented by Steam-Assisted Gravity Drainage and Cyclic Steam Stimulation. These technologies are based on steam injection into bitumen sand reservoirs for increase in reservoir in-situ temperature and in bitumen mobility. In oil sands reservoirs, efficiency for steam propagation is controlled mainly by reservoir geology. Accordingly, understanding of geological factors and characteristics of oil sands reservoir deposits is prerequisite for well-designed development planning and effective bitumen production. As significant geological factors and characteristics in oil sands reservoir deposits, this study suggests (1) pay of bitumen sands and connectivity, (2) bitumen content and saturation, (3) geologic structure, (4) distribution of mud baffles and plugs, (5) thickness and lateral continuity of mud interbeds, (6) distribution of water-saturated sands, (7) distribution of gas-saturated sands, (8) direction of lateral accretion of point bar, (9) distribution of diagenetic layers and nodules, and (10) texture and fabric change within reservoir sand body.

• Journal of Animal Environmental Science
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• v.18 no.2
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• pp.99-110
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• 2012

The purpose of the study is to collect basis data for to establish standard administrative processes of liquid fertilizer treatment. From this survey we could make out the key point of each step through a case of effective liquid manure treatment system in pig house. It is divided into six step; 1. piggery slurry management step, 2. Solid-liquid separation step, 3. liquid fertilizer treatment (aeration) step, 4. liquid fertilizer treatment (microorganism, recirculation and internal return) step, 5. liquid fertilizer treatment (completion) step, 6. land application step. From now on, standardization process of liquid manure treatment technologies need to be develop based on the six steps process.

• Food Science of Animal Resources
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• v.27 no.4
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• pp.392-400
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• 2007

This experiment was conducted to evaluate effects of various periods of rye silage feeding on the growth performance, blood characteristics, and carcass quality of finishing pigs. A total of sixteen [($Landrace{\times}Yorkshire{\times}Duroc$)] pigs (90.26 kg in average initial body weight) were tested in individual cages for a 30 day period. Dietary treatments included 1) CON (basal diet), 2) S10 (basal diet for 20 days and 3% rye silage for 10 days) 3) S20 (basal diet for 10 days and 3% rye silage for 20 days) and 4) S30 (3% rye silage for 30 days). There were no significant differences in the ADG and gain/feed ratio among the treatments(p>0.05), however the ADFI was higher in pigs fed the CON diet than with pigs fed diets with rye silage (p<0.05). The DM digestibility was higher with the S20 diet than with the S30 diet (p<0.05). With regard to blood characteristics, pigs fed rye silage had a significantly reduced cortisol concentration compared to pigs fed the CON diet (p<0.05). The backfat thickness was higher with the CON diet than with the S20 or S30 diets (p<0.05). Regarding the fatty acid contents of the leans, the C18:0 and total SFA were significantly higher with the CON diet than with the other diets (p<0.05). However, the C18:1n9, total MUFA and UFA/SFA levels were significantly lower with the CON diet than the other diets (p<0.05). Regarding the fatty acid contents of fat, the levels of C18:1n9 and MUFA were similar with the S20 and S30 diets, however, these levels were higher than with the CON or S10 diets (p<0.05). In conclusion, feed intake and DM digestibility were affected by rye silage, and the cortisol concentration, backfat thickness and fatty acid composition of pork were positively affected by feeding pigs rye silage.