• Korean Journal of Fisheries and Aquatic Sciences
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• v.38 no.1
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• pp.39-54
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• 2005

Seasonal variation in species composition of fish in the Geum River estuary was determined using monthly samples collected with a bag net from February to December 2003. Of a total of seventy-three species collected, four groups of fishes were distinguished. Estuarine fishes such as Chelon haematocheilus and Synechogobius hasta were collected almost all the seasons and predominated in abundance during cold months. Coastal fish species such as Sardinella zunasi, Konosirus punctatus, Engraulis japonicus, Johnius grypotus and Thryssa kammalensis were dominated from late spring to autumn. Their adults entered into the estuary in spring and a large number of their juveniles were collected in summer and autumn till moving out to deeper waters for over-wintering. A few freshwater fishes were collected when the freshwater was discharged during the rainy season. Anguilla japonica elvers (diadromous fish) and Coilia nasus (amphidromous fish) were collected in spring during their upstream migration. The principal component analysis revealed that the seasonal variation in species composition of fishes was principally determined by water temperature and/or water temperature related factors.

• Journal of Environmental Health Sciences
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• v.30 no.5 s.81
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• pp.455-468
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• 2004

The purpose of this paper is to understand the time series and origin of a chemical component and to compare the difference during yellow sand episodes for analysis $PM_{10}$ chemical components in the region of west in Korean Peninsula, 1999-2001. An annual mean concentration of $PM_{10}$ is $29.1\;{\mu}g/m^3$. A monthly mean and standard deviation of $PM_{10}$ concentration are very high in spring but there is no remarkably seasonal variation. Also, water soluble ionic component of $PM_{10}$ be influenced by double more total anion than total cation, be included $NO_{3}^-\;and\;SO_{4}^{2-}$ for the source of acidity and $NH_{4}^+$ to neutralize. Tracer metals of $PM_{10}$ slowly increases caused by emitted for soil and ocean (Fe, Al, Ca, Mg, Na) and Zn, Pb, Cu, Mn for anthropogenic source. According to method of enrichment factor (E.F) and statistics, assuming that the origin of metal component in $PM_{10}$ most of element in the Earth's crust e.g. Mg, Ca, Fe originates soil and Cu, Zn, Cd, Pb derives from anthropogenic sources. The ionic component for $Na^{+}\;Cl^-,\;Mg^{2+}\;and\;Ca^{2+}$ and Mg, Al, Ca, Fe originated by soil component largely increase during yellow sand period and then tracer metal component as Pb, Cd, Zn decrease. According to factor analysis, the first group is ionic component ($Na^+,\;Mg^{2+},\;Ca^{2+}$) and metal component (Na, Fe, Mn and Ni) be influenced by soil. The second group, Mg, Cr also be influenced by soil particle.

• Korean Journal of Construction Engineering and Management
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• v.20 no.3
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• pp.113-121
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• 2019

The death rate of industrial accidents per 10,000 people in Korea is two to three times higher than that of major countries. Falling accidents at the construction site happened to have caused the most deaths. Analysis of existing research and measures by national institutions showed that the industrial accident management concentrated on falling accidents was insufficient and the seasonal safety management measures were not enough. There is thus the need for research that provides detailed and enough information on falling accidents. This study, therefore, aims to overcome the limitations of existing research and safety management accident response using a methodology that provides the necessary information for the prevention of fall accidents by deriving seasonal crash characteristics of the construction site. In order to provide enough information, 387 cases of seasonal construction site falling were collected, which describes the causal relationship of accidents. Text mining using principal component analysis and cluster analysis was carried out. The analysis showed that: In the spring, snowfall and unreasonable operation of equipment including lifts were the major cause. In summer, most accidents were caused by form, insufficient safety inspection, and installation work. In autumn, weather factors such as wind and typhoon were the cause. In winter, material transportation, exterior wall work, and ignore safety precautions were the cause of the crash.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.5
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• pp.438-445
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• 1993

Spatial and seasonal variations of community structure of demersal fishes in Asan Bay were studied using seasonal samples collected by an otter trawl from autumn 1991 to summer 1992. For each sampling station, three trawl hauls were completed to obtain a reliable sample. Of 34 species identified, Cynoglossus joyneri, Johnius belengeri, Zoraces gillii and Thrissa koreana accounted for $93\%$ of the individuals collected. The former three dominant species were more abundant on the finer sediment of the inner bay than on the sandy bottom of the outer bay. Spatial variation of community structure of demersal fishes was analysed by principal component analysis using rank correlation. The community structure did not show a spatial difference, but a clear seasonal trend. This distribution pattern seems to be related significantly to the seasonal temperature fluctuation and to the active mixing of the water by strong tidal current of the bay.

• Korean Journal of Ichthyology
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• v.23 no.1
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• pp.37-45
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• 2011

Seasonal variation in species composition and abundance of fish larvae assemblages in the south-western Jinhae Bay were investigated monthly in 2009. During the study period, 49 larvae species belonging to 24 families were collected. The dominant species were Clupea pallasii, Hexagrammos otakii, Konosirus punctatus, Engraulis japonicus, Parablennius yatabei, and Omobranchus elegansei. These six species accounted for 92.7% of the total number of individuals collected. The number of species, number of individuals, and species diversity indices fluctuated with the seasons. The peak number of species and individuals occurred in July and January, respectively. Principal component analysis(PCA) and correlation analysis showed that variation in monthly water temperature could act as an indicator of seasonal variation in community structure and abundance of the dominant species; in particular, those of C. pallasii, H. otakii, E. japonicus and P. yatabei corresponded with the water temperature.

• 한국해양학회지
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• v.28 no.1
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• pp.1-16
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• 1993

The monthly mean sea levels at 48 stations located at the East and Yellow Seas coasts of Korea, Russia and Japan are processed to investigate seasonal sea level variations. The strong seasonal variations are found to be at the west coast of Korea (42.1 cm in Kunsan), in the region of the Korea strait and near the southern part of Primorye (30-33 cm); the weak ones near the southwestern coast of the Sakhalin Island (10-12 cm). Practically for the whole study area except the southwest Sakhalin, the general picture of the seasonal sea level changes is alike: the mean sea level rises in summer-autumn and falls in winter-spring. The spectral analysis of the records also shows that the seasonal oscillations strongly dominate in the sea level variations, more than 80% or total energy in the southern part of the investigated region and 50-70% in the northern part relate to these oscillations. The annal peak significantly prevails in spectra of the monthly sea levels for the majority of stations, the semiannual peak is also well manifested, but the seasonal peaks of higher order (corresponding to the periods of four and three months) reveal only at some records. The maximal amplitudes of annual component by a least square method are found at the Yellow Sea coast of Korea (20-21 cm) and also near the Japanese coast of the korea Strait (19-19 cm). The semiannual component has the maximal amplitudes (3-4 cm) near the south and southwestern coasts of the Sakhalin Island. The annual range of the sea levels is much weaker here than in the other regions, the relative investment of the seasonal oscillations in total energetic budget is only 35-40%, annual ($A_1$) and semiannual ($A_2$) components have nearly the same amplitude (seasonal factor $F=A_1/A_2=0.9-1.2$). On the basis of the present examination on sea level changes together with the results of Tomizawa et. al.(1984) the whole investigated area may be divided into 10 subregions, 2 of them are related to the Yellow Sea and Western part of the Korea Strait (Y1, Y2), the other ones (E1-E8) to the East Sea.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.730-738
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• 1994

The major advantage of the short-term load forecasting technique using general exponential smoothing is high accuracy and operational simplicity, but it makes large forecasting error when the load changes repidly. The paper has presented new technique to improve those shortcomings, and according to forecasted the technique proved to be valid for two years. The structure of load model is time function which consists of daily-and temperature-deviation component. The average of standard percentage erro in daily forecasting for two years was 2.02%, and this forecasting technique has improved standard erro by 0.46%. As relative coefficient for daily and seasonal forecasting is 0.95 or more, this technique proved to be valid.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1358-1365
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• 2010

Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.