• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1093-1099
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• 2016

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.201-208
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• 1995

In the last years there has been growing interest in concepts of positive dependence for families of random variables such that concepts are considerable us in deriving inequalities in probability and statistics. Lehman introdued various concepts of positive dependence for bivariate random variables. A much stronger notions of positive dependence were later considered by Esary, Proschan, and Walkup. Ahmed et al and Ebrahimi and Ghosh also obtained multivariate versions of various bivariate positive dependence as descrived by Lehman. See also Block al. Glaz and Johnson an Barlow and Proschan and the references there. Multivariate processes arise when instead of observing a single process we observe several processes, say $X_19t), \cdots, X_n(t)$ simultaneously. For example, in an engineering context we may want to study the simultaneous variation of current and voltage, or temperature, pressure and volume over time. In economics we may be interested in studying inflation rates and money supply, unemployment and interest rates. We could of course, study each quantity on its own and treat each as a separate univariate process. Although this would give us some information about each quantity it could never give information about the interrelationship between various quantities. This leads us to introduce some concepts of positive and for multivariate stochastic processes. The concepts of positive dependence have subsequently been extended to stochastic processes in different directions by many authors.

• Journal of the korean society of oceanography
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• v.39 no.2
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• pp.148-154
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• 2004

This study describes the realtime monitoring system for water quality conditions in coastal waters. Some issues on the data qualify control and quality analysis are examined along with examples of erroneous data. Three different cases of database produced by the realtime monitoring system are presented and analyzed, namely 1) hypoxic condition, 2) over-saturated D.O. and 3) short-term variability of temperature and D.O. In utilizing the realtime database, D.O. prediction and warning models are developed based on autoregressive stochastic process. The model is very simple, yet, users in various levels from powerful and useful with its ability to send warning messages to users in varous levels from governmental administrative staff to local fisherman, and give them some allowances to cope with the situation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.11-18
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• 2012

Daily temperature data produced by harmonic analysis of monthly climate summary have been used as an input to plant phenology model. This study was carried out to evaluate the performance of the harmonic based daily temperature data in prediction of major phenological developments and to apply the results in improving decision support for agricultural production in relation to the climate change scenarios. Daily maximum and minimum temperature data for a climatological normal year (Jan. 1 to Dec. 31, 1971-2000) were produced by harmonic analysis of the monthly climate means for Seoul weather station. The data were used as inputs to a thermal time - based phenology model to predict dormancy, budburst, and flowering of Japanese cherry in Seoul. Daily temperature measurements at Seoul station from 1971 to 2000 were used to run the same model and the results were compared with the harmonic data case. Leaving no information on annual variation aside, the harmonic based simulation showed 25 days earlier release from endodormancy, 57 days longer period for maximum cold tolerance, delayed budburst and flowering by 14 and 13 days, respectively, compared with the simulation based on the observed data. As an alternative to the harmonic data, 30 years daily temperature data were generated by a stochastic process (SIMMETEO + WGEN) using climatic summary of Seoul station for 1971-2000. When these data were used to simulate major phenology of Japanese cherry for 30 years, deviations from the results using observed data were much less than the harmonic data case: 6 days earlier dormancy release, 10 days reduction in maximum cold tolerance period, only 3 and 2 days delay in budburst and flowering, respectively. Inter-annual variation in phenological developments was also in accordance with the observed data. If stochastically generated temperature data could be used in agroclimatic mapping and zoning, more reliable and practical aids will be available to climate change adaptation policy or decision makers.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Structural Engineering and Mechanics
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• v.7 no.2
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• pp.217-224
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• 1999

A semi-stochastic process model of reliability was established for hyperbolic cooling towers subjected to combined loadings of wind force, self-weight, temperature loading. Effect of the soil-structure interaction on reliability was evaluated. By involving the gust factor, an equivalent static scheme was employed to convert the dynamic model to static model. The TR combination rule was used to consider relations between load responses. An analysis example was made on the 90M cooling tower of Maoming, Guangdong of China. Numerical results show that the design not including interaction turns to be conservative.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.588-590
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• 2008

The thermal signature of nucleation process is characterized by the induction time, the degree of supercooling, and the equilibrium temperature depression. The initiation of nucleation presents stochastic characteristics. The factors that affect nucleation are mechanical impact, ionic concentration, mineral surface characters, and pore size. Hydrate-bearing sediments behave mechanically like other cemented sediments. The data set has important implications for the calibration and interpretation of geophysical measurements and downhole logs collected in gas hydrate provinces, providing particular insight for the interpretation of P- and S-wave data and resistivity logs. In addition, laboratory formation history and ensuing pore-scale spatial distribution likely have a more pronounced effect on the macroscale mechanical properties of hydrate-bearing sediments

• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• Korean Chemical Engineering Research
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• v.47 no.4
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• pp.481-487
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• 2009

In this of study, effects of input air velocity(0.05~0.22 m/sec) and molten carbonate salt temperature ($870{\sim}970^{\circ}C$) on flow regime transition have been studied by adopting a drift-flux model of air holdup and a stochastic analysis of differential pressure fluctuations in an air-molten sodium carbonate salt two-phase system(molten salt oxidation process). Air holdup where the flow regime transition begins was determined by air holdup-drift flux plot. The air holdup value which the flow regime transition begins was increased with increasing molten carbonate salt temperature due to the decrease of viscosity and surface tension of molten carbonate salt. To characterize the flow regime transition more quantitatively, differential pressure fluctuation signals have been analyzed by adopting the stochastic method such as phase space portraits and Kolmogorov entropy, The Kolmogorov entropy decreased with an increasing of molten carbonate salt temperature but increased gradually with an increase in an air velocity, however, it exhibited different tendency with the flow regime and the air velocity value which flow regime transition begins was same to the results of drift-flux analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.215-224
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• 2008

The effect of streamflow considering future land use change and vegetation index information by climate change scenario was assessed using SLURP (Semi-distributed Land-Use Runoff Process) model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for the upstream watershed ($260.4km^2$) of Gyeongan water level gauging station. By applying CA-Markov technique, the future land uses (2030, 2060, 2090) were predicted after test the comparison of 2004 Landsat land use and 2004 CA-Markov land use by 1996 and 2000 land use data. The future land use showed a tendency that the forest and paddy decreased while urban, grassland and bareground increased. The future vegetation indices (2030, 2060, 2090) were estimated by the equation of linear regression between monthly NDVI of NOAA AVHRR images and monthly mean temperature of 5 years (1998-2002). Using CCCma CGCM2 simulation result based on SRES A2 and B2 scenario (2030s, 2060s, 2090s) of IPCC and data were downscaled by Stochastic Spatio-Temporal Random Cascade Model (SST-RCM) technique, the model showed that the future runoff ratio was predicted from 13% to 34% while the runoff ratio of 1999-2002 was 59%. On the other hand, the impact on runoff ratio by land use change showed about 0.1% to 1% increase.