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A Study on the Effect of Environmental Pollution on the Biomass Productivity of Paulownia coreana (환경오염(環境汚染)이 오동나무인공림(人工林)의 물질생산(物質生産)에 미치는 영향(影響)에 관한 연구(硏究))

  • Kim, Tae Wook;Lee, Kyong Jae;Park, In Hyeop
    • Journal of Korean Society of Forest Science
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    • v.58 no.1
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    • pp.8-16
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    • 1982
  • To study the comparison of aboveground biomass of paulownia coreana Uyeki of 6-year-old, located in Seongju of non-attacked forest and Ulsan of damaged forest by the air pollution were selected. Ten sample trees in Seongju district and seven trees in Ulsan selected taking account of DBH were measured for 16 trees in total within a $10{\times}10m$ experimental plot. The diagram of oven-dry weight distribution of stem, branch and needle for each 1m segment was constructed. The logarithmic regression equations between dry weight of each component and the two-variables, DBH and tree height, combined term were presented. IF the estimations are extended to a hectare area stand, it contains 47.49 tons of aboveground biomass in Seongju district and 19.05 tons of it in Ulsan. The annual net productions were 11.64 tons of above 2.29 kg/kg/yr in Ulsan and the efficiency of leaf to produce stem was 2.99kg/kg/yr in Seongju and 0.83kg/kg/yr in Ulsan.

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Rough Set Analysis for Stock Market Timing (러프집합분석을 이용한 매매시점 결정)

  • Huh, Jin-Nyung;Kim, Kyoung-Jae;Han, In-Goo
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.77-97
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    • 2010
  • Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

Ecological Changes of Insect-damaged Pinus densiflora Stands in the Southern Temperate Forest Zone of Korea (I) (솔잎혹파리 피해적송림(被害赤松林)의 생태학적(生態学的) 연구(研究) (I))

  • Yim, Kyong Bin;Lee, Kyong Jae;Kim, Yong Shik
    • Journal of Korean Society of Forest Science
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    • v.52 no.1
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    • pp.58-71
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    • 1981
  • Thecodiplosis japonesis is sweeping the Pinus densiflora forests from south-west to north-east direction, destroying almost all the aged large trees as well as even the young ones. The front line of infestation is moving slowly but ceaselessly norhwards as a long bottle front. Estimation is that more than 40 percent of the area of P. densiflora forest has been damaged already, however some individuals could escapes from the damage and contribute to restore the site to the previous vegetation composition. When the stands were attacked by this insect, the drastic openings of the upper story of tree canopy formed by exclusively P. densiflora are usually resulted and some environmental factors such as light, temperature, litter accumulation, soil moisture and offers were naturally modified. With these changes after insect invasion, as the time passes, phytosociologic changes of the vegetation are gradually proceeding. If we select the forest according to four categories concerning the history of the insect outbreak, namely, non-attacked (healthy forest), recently damaged (the outbreak occured about 1-2 years ago), severely damaged (occured 5-6 years ago), damage prolonged (occured 10 years ago) and restored (occured about 20 years ago), any directional changes of vegetation composition could be traced these in line with four progressive stages. To elucidate these changes, three survey districts; (1) "Gongju" where the damage was severe and it was outbroken in 1977, (2) "Buyeo" where damage prolonged and (3) "Gochang" as restored, were set, (See Tab. 1). All these were located in the south temperate forest zone which was delimited mainly due to the temporature factor and generally accepted without any opposition at present. In view of temperature, the amount and distribution of precipitation and various soil factor, the overall homogeneity of environmental conditions between survey districts might be accepted. However this did not mean that small changes of edaphic and topographic conditions and microclimates can induce any alteration of vegetation patterns. Again four survey plots were set in each district and inter plot distance was 3 to 4 km. And again four subplots were set within a survey plot. The size of a subplot was $10m{\times}10m$ for woody vegetation and $5m{\times}5m$ for ground cover vegetation which was less than 2 m high. The nested quadrat method was adopted. In sampling survey plots, the followings were taken into account: (1) Natural growth having more than 80 percent of crown density of upper canopy and more than 5 hectares of area. (2) Was not affected by both natural and artificial disturbances such as fire and thinning operation for the past three decades. (3) Lower than 500 m of altitude (4) Less than 20 degrees of slope, and (5) Northerly sited aspect. An intensive vegetation survey was undertaken during the summer of 1980. The vegetation was devided into 3 categories for sampling; the upper layer (dominated mainly by the pine trees), the middle layer composed by oak species and other broad-leaved trees as well as the pine, and the ground layer or the lower layer (shrubby form of woody plants). In this study our survey was concentrated on woody species only. For the vegetation analysis, calculated were values of intensity, frequency, covers, relative importance, species diversity, dominance and similarity and dissimilasity index when importance values were calculated, different relative weights as score were arbitrarily given to each layer, i.e., 3 points for the upper layer, 2 for the middle layer and 1 for the ground layer. Then the formula becomes as follows; $$R.I.V.=\frac{3(IV\;upper\;L.)+2(IV.\;middle\;L.)+1(IV.\;ground\;L.)}{6}$$ The values of Similarity Index were calculated on the basis of the Relative Importance Value of trees (sum of relative density, frequency and cover). The formula used is; $$S.I.=\frac{2C}{S_1+S_2}{\times}100=\frac{2C}{100+100}{\times}100=C(%)$$ Where: C = The sum of the lower of the two quantitative values for species shared by the two communities. $S_1$ = The sum of all values for the first community. $S_2$ = The sum of all values for the second community. In Tab. 3, the species composition of each plot by layer and by district is presented. Without exception, the species formed the upper layer of stands was Pinus densiflora. As seen from the table, the relative cover (%), density (number of tree per $500m^2$), the range of height and diameter at brest height and cone bearing tendency were given. For the middle layer, Quercus spp. (Q. aliena, serrata, mongolica, accutissina and variabilis) and Pinus densiflora were dominating ones. Genus Rhodedendron and Lespedeza were abundant in ground vegetation, but some oaks were involved also. (1) Gongju district The total of woody species appeared in this district was 26 and relative importance value of Pinus densiflora for the upper layer was 79.1%, but in the middle layer, the R.I.V. for Quercus acctissima, Pinus densiflora, and Quercus aliena, were 22.8%, 18.7% and 10.0%, respectively, and in ground vegetation Q. mongolica 17.0%, Q. serrata 16.8% Corylus heterophylla 11.8%, and Q. dentata 11.3% in order. (2) Buyeo district. The number of species enumerated in this district was 36 and the R.I.V. of Pinus densiflora for the uppper layer was 100%. In the middle layer, the R.I.V. of Q. variabilis and Q. serrata were 8.6% and 8.5% respectively. In the ground vegetative 24 species were counted which had no more than 5% of R.I.V. The mean R.I.V. of P.densiflora ( totaling three layers ) and averaging four plots was 57.7% in contrast to 46.9% for Gongju district. (3) Gochang-district The total number of woody species was 23 and the mean R.I.V. of Pinus densiflora was 66.0% showing greater value than those for two former districts. The next high value was 6.5% for Q. serrata. As the time passes since insect outbreak, the mean R.I.V. of P. densiflora increased as the following order, 46.9%, 57.7% and 66%. This implies that P. densiflora was getting back to its original dominat state again. The pooled importance of Genus Quercus was decreasing with the increase of that for Pinus densiflora. This trend was contradict to the facts which were surveyed at Kyonggi-do area (the central temperate forest zone) reported previously (Yim et al, 1980). Among Genus Quercus, Quercus acutissina, warm-loving species, was more abundant in the southern temperature zone to which the present research is concerned than the central temperate zone. But vice-versa was true with Q. mongolica, a cold-loving one. The species which are not common between the present survey and the previous report are Corpinus cordata, Beltala davurica, Wisturia floribunda, Weigela subsessilis, Gleditsia japonica var. koraiensis, Acer pseudosieboldianum, Euonymus japonica var. macrophylla, Ribes mandshuricum, Pyrus calleryana var. faruiei, Tilia amurensis and Pyrus pyrifolia. In Figure 4 and Table 5, Maximum species diversity (maximum H'), Species diversity (H') and Eveness (J') were presented. The Similarity indices between districts were shown in Tab. 5. Seeing Fig. 6, showing two-dimensional ordination of polts on the basis of X and Y coordinates, Ai plots aggregate at the left site, Bi plots at lower site, and Ci plots at upper-right site. The increasing and decreasing patterns as to Relative Density and Relative Importance Value by genus or species were given in Fig. 7. Some of the patterns presented here are not consistent with the previously reported ones (Yim, et al, 1980). The present authors would like to attribute this fact that two distinct types of the insect attack, one is the short war type occuring in the south temperate forest zone, which means that insect attack went for a few years only, the other one is a long-drawn was type observed at the temperate forest zone in which the insect damage went on continuously for several years. These different behaviours of infestation might have resulted the different ways of vegetational change. Analysing the similarity indices between districts, the very convincing results come out that the value of dissimilarity index between A and B was 30%, 27% between B and C and 35% between A and C (Table 6). The range of similarity index was obtained from the calculation of every possible combinations of plots between two districts. Longer time isolation between communities has brought the higher value of dissimilarity index. The main components of ground vegetation, 10 to 20 years after insect outbreak, become to be consisted of mainly Genus Lespedeza and Rhododendron. Genus Quercus which relate to the top dorminant state for a while after insect attack was giving its place to Pinus densiflora. It was implied that, provided that the soil fertility, soil moisture and soil depth were good enough, Genus Quercuss had never been so easily taken ever by the resistant speeies like Pinus densiflora which forms the edaphic climax at vast areas of forest land. Usually they refer Quercus to the representative component of the undisturbed natural forest in the central part of this country.

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