• Journal of Environmental Health Sciences
• /
• v.30 no.5 s.81
• /
• pp.378-387
• /
• 2004

This study was carried to investigate the emission characteristics of mercury from domestic and industrial MSW (municipal solid waste) incinerator stacks. The mercury concentration levels of flue gas from 32 MSW incinerators stacks selected were above the criteria level ($5{\mu}g/S\;m^3$). MSWI facilities exceeding the criteria levels in Korea are due to the poor units comparison of combustion chamber(CC)-cyclone(CY)-stack. So, the mercury from MSW incinerators stack were suspected to contaminate the natural system unless the MSW incinerators were properly controlled. Mean-while, the relationship between mercury concentration and temperature of flue gas in MSW incinerator stacks were examined at two temperature ranges (Group A : $29.85{\sim}327.63^{\circ}C$, Group B : $446.9{\sim}848.15^{\circ}C$). The mercury concentration in flue gas with high temperature range was higher than that of flue gas with low temperature rage. This mean that the temperature of flue gas plays an important role in mercury control in MSW incinerator. The emission characteristics oi mercury was also evaluated by using the correlation matrix between the mercury and NOx, $PM_{10}$, moisture (MO.) at both low temperature and high temperature flue gas ranges. The mercury concentration was mainly affected by NOx, $PM_{10}$. moisture (MO.) at low temperature range, while the mercury concentration at high temperature flue gas was mainly affected by NOx, moisture (MO.). From these results, it was suggested that the temperature of cooling system and the air pollution control device should be properly regulated in order to control mercury of flue gas in MSWI incinerator.

• The Korean Journal of Quaternary Research
• /
• v.3 no.1
• /
• pp.55-68
• /
• 1989

This paper presents examples of the computer-aided zonation and correlation of pollen data from the Late-glacial to Holocene stratigraphic sequences at four sites in central Iowa, U.S.A. Spearman's rank correlation coefficient matrix and first four components of Principal components analysis plotted in a stratigraphic order are combined to provide an excellent zonation of the pollen data at each site. Correlation of the four pollen sequences are conducted by Principal components analysis of the data sets combined in one. The first and second principal components successfully provide correlation lines that match fairly closely the zone boundaries of each pollen sequence. The third and fourth components, in contrast, are greatly different from site to site, representing the unique pollen assemblages at each site.

• Communications for Statistical Applications and Methods
• /
• v.17 no.1
• /
• pp.39-45
• /
• 2010

As the value of environment is increasing, the water quality has been a matter of interest to the nation and people. Research on water quality has been widely studied, but focused on geographical characteristic and river characteristics like inflow, outflow, quantity and speed of water. In this paper, two approaches to measure the similarity of sampling sites by using water quality data are discussed and compared with two-years empirical data of Yongdam-Dam. The existing method has calculated their similarities with principal component scores. The proposed approach in this paper use correlation matrix of water quality related variables and MDS for measuring the similarity, which is shown to be better in the sense of being clustering which is identical to geographical clustering since it can consider the time series pattern of water quality.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.38 no.9
• /
• pp.616-623
• /
• 2001

Effects of the grating period fluctuation on the wavelength distribution have been studied by an effective index transfer matrix method in quarter wavelength shifted (QWS) DFB lasers. The wavelength distribution is expressed by a probability density that is an analytical function of the correlation coefficient and normalized standard deviation of the grating period fluctuation. The probability density function of wavelength distribution is shown to be nearly Gaussian, and its standard deviation increases with normalized standard deviation of the grating period fluctuation, and decreases with the negative correlation between adjacent half-periods.

• Korean Journal of Microbiology
• /
• v.23 no.3
• /
• pp.215-222
• /
• 1985

Distribution pattern and activity of heterotrophec bacteria were measured in the mudflat of Nakdong river estuary. In March and June, 1985, community sizes of amylolytic, lipolytic and proteolytic bacteria as well as total viable counts were measured. Vertical distribution of bacterial community size increased a few orders of magnitude from Narch to June. Heterotrophic activity was estimated in turnover time with $U-[^{14}C]-glucose$. Turnover time reduced considerably in June compared to that of March. To sxamine correlations for measured bacterial groups, turnover time and environmental factors, correlation coefficient matrix was obtained. These measured characteristics did not consistently correlate well with one another.

• Korean Management Science Review
• /
• v.12 no.1
• /
• pp.51-60
• /
• 1995

This paper presents a workload based model and cell design support system (CDSS) in manufacturing cell design. The proposed model consider manufacturing factor such as machine capacity, production volume, process time, and cell size. Based on those information, workload is calculated and according to the workload, the relationship between machine and part is represented by the workload matrix. To form the manufacturing cell, correlation similarity coefficient (CSC) among machines are calculated and a pair of machines that has the highest value of CSC is assigned to a machine cell. Repeat the above steps until the desired manufacturing cells are obtained. Finally, a cell design support system that could increase the efficiency in the application of a proposed model is developed. The proposed model and CDSS are illustrated by a numerical example.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.336-341
• /
• 2004

This research investigates the feasibility of ultrasonic microscope for nondestructive assessment of thermal degradation in artificially aged commercial Co-base superalloy, FSX414. This alloy has been used for high temperature structure applications such as stationary gas turbine blade and nozzle chamber in fossil plant. Microstructural change was found that the fine carbides became coarser and spheroidized in matrix as aging time increased. The leaky surface acoustic wave velocity gradually decreases by a maximum of 4.7% with increasing aging time up to 4,000hours. However, the longitudinal wave velocity has a little change. Also, it has a good correlation between leaky surface acoustic wave velocity and Vickers hardness. Consequently, LSAW can be used to examine the degree of degradation in thermally aged Co-base superalloy.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1002-1004
• /
• 1996

In this paper, presented are the results of observability analysis for the vehicle maneuver during In-Flight Alignment of SDINS. The target system for observability analysis is 10th order one. Three trajectories for IFA are considered. To analyze the observability of the time varying system, correlation coefficient is used and to measure the degree of observability of the given system, simulation is carried out using covariance matrix. The results of simulation show that trajectories which are having continuous changes in attitude and acceleration of system is superior to straight trajectory in correcting navigation errors.

• Composites Research
• /
• v.13 no.1
• /
• pp.1-10
• /
• 2000

During the compression molding process of the continuous fiber-reinforced polymeric composites, two main problems such as fiber-matrix separation and fiber orientation are produced by the difference of flow velocity. Molded parts are lead to be nonhomogeneous and anisotropic. As the mechanical property of the products are dependent on the separation and orientation, it is important to research the fiber mat structure and molding condition. If the fiber mat structure is changed by the increment of needling, the separation decreases and after compression molding the orientation is easily aligned. As it were, the compression moldability is good. But the defects as tears, thin thickness are produced in the products. Therefore, it is important to clarify the moldability in relation to the usage of products and the expenses of produce on the actual process. Therefore we must make the measurement methods that can define the moldability of products. In this research, the effects of the fiber mat structure(NP = 0, 5, 10, 25, 50 punches/$cm^2$) and the mold geometry($r_p$ = 1, 25, 50 mm) on the moldability of products were discussed. We investigated the case of one-dimensional flow in order to obtain the degree of nonhomogeneity and the fiber orientation function. In result, we could gain the correlation coefficient of the continuous fiber-reinforced polymeric composites. Also we experimented on the cup-type compression molding which was appeared the wrinkle on the flange part by the complex stress condition in order to gain the degree of nonhomogeneity and area ratio. In result, the moldability of products was expressed as the correlation coefficient and area ratio.

• The Journal of Engineering Geology
• /
• v.33 no.4
• /
• pp.673-687
• /
• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.