• Korean Journal of Environment and Ecology
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• v.12 no.1
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• pp.1-8
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• 1998

To investigate the structure and the conservation strategy of Taxus cuspidata forest at Janggunbong area in Taebaeksan, 18 plots(20m$\times $20m) set up with random sampling method were surveyed. Major woody species growing with Taxus cuspidata were Acer pseudo- sieboldianum, Euonymus macroptera, Prunus padus, Abies nephrolepis. High positive correlations was proved between abies nephrolepis and Pinus doraiensis, Betula ermanii and Magnolia sieboldii, and high negative correlations was proved between Acer pseudo- sieboldianum and Tripterygium regelii, Weigela subsessilis and Acer tschonoskii var. rubripes, Taxus cuspidata and Acer tschonoskii var. rubripes. Species diversity(H') of investigated area was calculated 2.3015(0.9991) and this value was relatively lower than that of other subalpine forest. Taxus cuspidata trees were severely stressed and vigor-lossed and 8.22% of surveyed Tacus cuspidata trees were dead tree. Dead trees were distributed mainly in 20~60cm DBH class and a few seedlings of Taxus cuspidata were observed. To conserve Taxus cuspidata forest at Janggunbong area in Taebaeksan, intensive research, prescription on the decline of Taxus cuspidata and proper forest management were needed.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.311-325
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• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• The Journal of Economics, Marketing and Management
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• v.3 no.3
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• pp.7-23
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• 2015

Women are more disadvantaged than men in many fronts and this confines them to informal sector livelihood activities. Any attempt to improve women's economic status will require information on the organization, cost and returns to investment in the livelihood activities in which they predominate. This is the issue for this study which compared yield performance in artisanal fishing and fresh fish marketing. Primary data collected through multi-stage sampling method were analyzed using inferential statistics, budgeting and regression models. Empirical findings revealed that about 75.0% of fisher folks either had no formal education or acquired only primary school education while 50.0% of marketers had secondary school education. The budgeting model revealed fisher-folks' and marketers' annual net profit to be N2,882,626.00 and N640,227.00, respectively. Profit from fishing was significantly higher than that of fish marketing. At 53.2% for fishing and 40.3% for marketing, returns to investment was better in fishing. Regression model results showed the significant factors influencing returns to each livelihood strategy to include fishing ground, distance covered and years of experience. The major constraint faced by operators of both livelihoods groups was insufficient credit. Despite this, the livelihood strategies were shown to be profitable ventures which contributed to households' consumption expenditure. Organizing women informal sector operators into groups to enhance access to government support and formal credit are recommended for improving livelihood strategy performance.

• Economic and Environmental Geology
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• v.35 no.6
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• pp.545-552
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• 2002

In order to investigate the influence of nuclear power plant operation on its nearby environment, soil, stream and marine sediment samples were collected in the vicinity of the Youngkwang Nuclear Power Plant in Korea, and analyzed for artificial and natural radionuclide radioactivity. From the analytical result, Cs-137 was detected in most soil samples. but it may have been derived fiom past nuclear weapon tests because Cs-134 having short half-live was not detected. The radioactivities of Cs-137 in the sediment samples were also detected which are within the normal range in the sediments based upon the published literature between 1997 and 1999. For the quality control of radioactivity analysis of environmental samples, sets of marine sediments in the Gamami area were analyzed using two HPGe Gamma-ray Spectroscopes (30% and 45%) according to the geostatistical sampling strategy, and Cs-137 and K-40 results were interpreted by analysis of variance (ANOVA). In the two-way ANOVA, variances derived from the geochemical variation were significant, but errors from sampling and analytical procedures are negligible. In conclusion. all the radioanalytical procedures of this study including sampling are validated to be acceptable.

• Korean Journal of Veterinary Service
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• v.26 no.1
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• pp.81-88
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• 2003

A cross-sectional epidemiological study using a multistage sampling strategy in slaughter cattle in Mymensingh, Bangladesh was conducted during September 2001 to April 2002 to study distributions and risk factors of specific pulmonary lesions and diseases. The pulmonary lesions and diseases were diagnosed on the basis of macroscopic and microscopic pathological and parasitological findings. The frequency distribution of pulmonary lesions and diseases in slaughter cattle was found to vary among categories of the study variables. The risk factors identified on statistical basis were male cattle and ${\leq}3$ years old cattle and summer in congestion, slightly thin physical condition and summer season in pulmonary emphysema, slightly thin physical condition in parasitic bronchitis as well as female cattle, autumn and rainy seasons in pulmonary hydatidosis. The population impact and etiologic significance of summer season on pulmonary congestion and emphysema was more important than that of adult male cattle. The population impact and etiologic significance of autumn season on pulmonary hydatidosis was more important than that of female cattle during rainy season. Population impact of slightly thin cattle on parasitic bronchitis was poor.

• Korean Journal of Environment and Ecology
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• v.6 no.2
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• pp.127-133
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• 1993

To investigate the structure and the conservation strategy of natural forest at Dosolbong area in Mt. Sobaek, 20 plots(400$m^2$) set up with random sampling method. Three groups were classified by cluster analysis. High positive correlations was proved between Acer pseudo-sieboldianum and Tilia amurensis, Weigela subsessilis, Corylus heterophylla var. thunbergii and Stephanandra incisa, Lespedeza bicolor and Styrax obassia, and high negative correlations was proved between Rhododendron schlippenbachii and Symplocos chinensis, Carpinus cordata. Species diversity(H') of investigated area was calculated 2.2521-2.3772. Leontopodium japonicum community at Dosolbong area in Mt. Sobaek should be conserved.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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• pp.115-125
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• 2014

In this study, particle composition data for $PM_{10}$ samples were collected every 3 days at Seoul, Korea from August 2006 to November 2007, and were analyzed to provide source identification and apportionment. A total of 164 samples were collected and 21 species (15 inorganic species, 4 ionic species, OC, and EC) were analyzed by particle-induced x-ray emission, ion chromatography, and thermal optical transmittance methods. Positive matrix factorization (PMF) was used to develop source profiles and to estimate their mass contributions. The PMF modeling identified nine sources and the average mass was apportioned to secondary nitrate (9.3%), motor vehicle (16.6%), road salt (5.8%), industry (4.9%), airborne soil (17.2 %), aged sea salt (6.2%), field burning (6.0%), secondary sulfate (16.2%), and road dust (17.7%), respectively. The nonparametric regression (NPR) analysis was used to help identify local source in the vicinity of the sampling area. These results suggest the possible strategy to maintain and manage the ambient air quality of Seoul.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.6
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• pp.737-749
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• 2013

As more famous and luxurious fashion brands enter the online market, the changes in the online market include those in the composition of merchandise, price image, and consumer behavior. Focusing on these changes, this study examines the relation of consumer price attitude and impulse buying behavior towards internet apparel purchases. Data were obtained from 377 males and females in their 20s-40s who purchased apparel from an internet mall. Convenience sampling through the internet was performed. Collected data were analyzed by descriptive statistics, factor analysis, t-test, ANOVA, Duncan test, and regression analysis using SPSS for Windows 12.0. The results of this study are as follows. First, four dimensions of consumer price attitudes towards internet apparel purchases were found that included price-quality/prestige, sale, value for money, and low price orientation. Second, the influence of consumer price attitude on impulse buying is significant. As the price attitude of price-quality/prestige orientation and sale orientation increases, impulse buying orientation is stronger. Third, there are partially significant differences on the sub-dimensions of consumer price attitude and the influence of price attitude on impulse buying by gender and age. This study will be of help to internet companies by providing information in regards to a price attitude-based marketing strategy and an adequate response to customer impulse buying.