• Korean Journal of Food Science and Technology
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• v.38 no.4
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• pp.589-593
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• 2006

The objective of this study was to evaluate the use of fuzzy math strategy to calculate variability and uncertainty in quantitative risk assessment. We compared the propagation of uncertainty using fuzzy math simulation with Monte Carlo simulation. The risk far Listeria monocytogenes contamination was estimated for carcass and processed pork by fuzzy math and Monte Carlo simulations, respectively. The data used in these simulations were taken from a recent report on pork production. In carcass, the mean values for the risk from fuzzy math and Monte Carlo simulations were -4.393 log $CFU/cm^2$ and -4.589 log $CFU/cm^2$, respectively; in processed pork, they were -4.185 log $CFU/cm^2$ and -4.466 log $CFU/cm^2$ respectively. The distribution of values obtained using the fuzzy math simulation included all of the results obtained using the Monte Carlo simulation. Consequently, fuzzy math simulation was found to be a good alternative to Monte Carlo simulation in quantitative risk assessment of pork production.

• Korean Journal of Computational Design and Engineering
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• v.18 no.1
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• pp.21-27
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• 2013

Presented in the paper is a log data based modeling method for effective construction of a virtual plant model which can be used for the virtual PLC (Programmable Logic Controller) simulation. For the PLC simulation, the corresponding virtual plant, consisting of virtual devices, is required to interact with the input and output symbols of a PLC. In other words, the behavior of a virtual device should be the same as that of the real device. Conventionally, the DEVS (Discrete Event Systems Specifications) formalism has been used to represent the behavior a virtual device. The modeling using DEVS formalism, however, requires in-depth knowledge in the simulation area, as well as the significant amount of time and efforts. One of the key ideas of the proposed method is to generate a plant model based on the log data obtained from the production system. The proposed method is very intuitive, and it can be used to generate the full behavior model of a virtual device. The proposed approach was applied to an AGV (Automated Guided Vehicle).

• Journal of the Korean Institute of Gas
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• v.20 no.1
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• pp.13-22
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• 2016

This paper presents development of an appropriate procedure and flow chart to analyze shale gas production data obtained from a multi-fractured horizontal well according to flow characteristics in order to calculate an estimated ultimate recovery. Also, the technical considerations were proposed when a rate transient analysis was performed with field production data occurred to only $1^{st}$ transient flow. If production data show the $1^{st}$ transient flow from log-log and square root time plot analysis, production forecasting must be performed by applying different method as before and after of the end of $1^{st}$ linear flow. It is estimated by an area of stimulated reservoir volume which can be calculated from analysis results of micro-seismic data. If there are no bottomhole pressure data or micro-seismic data, an empirical decline curve method can be used to forecast production performance. If production period is relatively short, an accuracy of production data analysis could be improved by analyzing except the early production data, if it is necessary, after evaluating appropriation with near well data. Also, because over- or under-estimation for stimulated reservoir volume could take place according to analysis method or analyzer's own mind, it is necessary to recalculate it with fracture modeling, reservoir simulation and rate transient analysis, if it is necessary, after adequacy evaluation for fracture stage, injection volume of fracture fluid and productivity of producers.

• Journal of Food Hygiene and Safety
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• v.13 no.3
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• pp.278-293
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• 1998

The purposes of this study were to develop Hazard Analysis Critical Control Point-based standardized recipe applicable to cook/chilled Broiled Spanish Mackerel in school foodservice operations and to establish reasonable shelf-life limits by assessing food quality during chilled storage period of 5 days. HACCP for the production of menu items was identified in simulation study. At each critical control point, time-temperature profile was recorded and microbiological analysis was done. Also chemical analyses and sensory evaluation were conducted for 5 days of chilled storage. The results of time-temperature measurement of Broiled Spanish Mackerel by each production phase showed satisfactory condition that met the standards. Broiled Spanish Mackerel showed excellent microbiological quality from raw ingredient phase ($TPC:2.58{\pm}0.12\;Log\;CFU/g$) to holding phase ($TPC:2.70{\pm}0.42\;Log\;CFU/g$). Coliform (0.84 Log MPN/g) and fecal coliform (0.84 Log MPN/g) were detected from marinating phase ($TPC:3.82{\pm}0.52\;Log\;CFU/g$). After heating, only few mesophiles were detected ($TPC:1.83{\pm}0.49\;Log\;CFU/g$). No psychrophiles, coliforms and fecal coliforms were detected. In the phases after rapid chilling, during chilled storage and after reheating and distribution, almost none of the above microbes were detected. Salmonella and Listeria monocytogenes were not detected in all production phases. The pH immediately after cooking was 6.65 and then increased significantly to 6.81 on the third day of chilled storage (p<0.001). Acid value did not show significant changes while total volatile based nitrogen (TVBN) dramatitically increased during storage periods (p<0.01). In the result of sensory evaluation, general acceptability points had been rated high in the first day of storage, and then, the points were decreased significantly on the third day (p<0.05). General acceptability points ranged from 8.86 to 10.68. Accordingly, Broiled Spanish Mackerel is highly recommendable cook/chill system. Considering the DHSS standards for storage, the ideal shelf-life recommended for Broiled Spanish Mackerel is within 4 days excluding cooking day. For Broiled Spanish Mackerel, critical control points were purchasing and receiving of frozen Spanish Mackerel, heating, chilling, chilled storage, reheating and distribution.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.8
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• pp.1188-1196
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• 2016

This study evaluated the risk of Clostridium perfringens (C. perfringens) foodborne illness from natural and processed cheeses. Microbial risk assessment in this study was conducted according to four steps: hazard identification, hazard characterization, exposure assessment, and risk characterization. The hazard identification of C. perfringens on cheese was identified through literature, and dose response models were utilized for hazard characterization of the pathogen. For exposure assessment, the prevalence of C. perfringens, storage temperatures, storage time, and annual amounts of cheese consumption were surveyed. Eventually, a simulation model was developed using the collected data and the simulation result was used to estimate the probability of C. perfringens foodborne illness by cheese consumption with @RISK. C. perfringens was determined to be low risk on cheese based on hazard identification, and the exponential model ($r=1.82{\times}10^{-11}$) was deemed appropriate for hazard characterization. Annual amounts of natural and processed cheese consumption were $12.40{\pm}19.43g$ and $19.46{\pm}14.39g$, respectively. Since the contamination levels of C. perfringens on natural (0.30 Log CFU/g) and processed cheeses (0.45 Log CFU/g) were below the detection limit, the initial contamination levels of natural and processed cheeses were estimated by beta distribution (${\alpha}1=1$, ${\alpha}2=91$; ${\alpha}1=1$, ${\alpha}2=309$)${\times}$uniform distribution (a = 0, b = 2; a = 0, b = 2.8) to be -2.35 and -2.73 Log CFU/g, respectively. Moreover, no growth of C. perfringens was observed for exposure assessment to simulated conditions of distribution and storage. These data were used for risk characterization by a simulation model, and the mean values of the probability of C. perfringens foodborne illness by cheese consumption per person per day for natural and processed cheeses were $9.57{\times}10^{-14}$ and $3.58{\times}10^{-14}$, respectively. These results indicate that probability of C. perfringens foodborne illness by consumption cheese is low, and it can be used to establish microbial criteria for C. perfringens on natural and processed cheeses.

• Geomechanics and Engineering
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• v.15 no.1
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• pp.677-686
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• 2018

Aimed at serious casing damage problem during the process of oilfield development by injecting water, based on seepage mechanics, fluid mechanics and the theory of rock mechanics, the multi-physics coupling theory was also taken into account, the mathematical model for production of petroleum with water flooding was established, and the method to solve the coupling model was presented by combination of Abaqus and Eclipse software. The Q9G3 block in Jibei oilfield was taken for instance, the well log data and geological survey data were employed to build the numerical model of Q9G3 block, the method established above was applied to simulate the evolution of seepage and stress. The production data was imported into the model to conduct the history match work of the model, and the fitting accuracy of the model was quite good. The main mechanism of casing damage of the block was analyzed, and some wells with probable casing damage problem were pointed out, the displacement of the well wall matched very well with testing data of the filed. Finally, according to the simulation results, some useful measures for preventing casing damage in Jibei oilfield was proposed.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.772-779
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• 2005

The objectives of this study were to examine problems with using F$_1$ data by simulation, association of marker loci and QTL from crosses of inbred parental lines and to enumerate the preliminary characterization of genetic superiority within inbred parental lines. In this study, the association between markers for QTL used as covariates and estimates of variance components due to effects of lines was investigated through computer simulation. The effects of size of population to develop inbred lines and initial frequencies and magnitudes of effects of QTL were also considered. Results show that estimates of variance components due to line effects are influenced by including marker information as covariates in the model for analysis. Estimates of line variance were increased by adding marker information into the analysis, because negative covariances between effects associated with the markers and the remaining effects associated with other loci existed. However, the fit of the model as indicated by the log likelihood improved by adding more markers as covariates into the analysis. Marker assisted selection will be beneficial when markers explain unexplained genetic difference during selection procedure. Markers can be used to identify QTLs affecting traits, and to select for favorable QTL alleles. To efficiently use genetic markers, location of markers at the genome must be identified. The estimates of variance due to effects of with and without marker information used as covariates in the analysis were investigated. The estimates of line variances were always increased when markers were included as covariates for the model because a negative covariance were existed.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.203-214
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• 2000

Health risk assessment is applied to streamlining LCA(Life Cycle Assessment) using Monte carlo simulation for probabilistic/stochastic exposure and risk distribution analysis caused by data variability and uncertainty. A case study was carried out to find benefits of this application. BTC(Benzene, Trichloroethylene, Carbon tetrachloride mixture alias) personal exposure cases were assumed as production worker(in workplace), manager(in office) and business man(outdoor). These cases were different from occupational retention time and exposure concentration for BTC consumption pattern. The result of cancer risk in these 3 scenario cases were estimated as $1.72E-4{\pm}1.2E+0$(production worker; case A), $9.62E-5{\pm}1.44E-5$(manger; case B), $6.90E-5{\pm}1.16E+0$(business man; case C), respectively. Portions of over acceptable risk 1.00E-4(assumed standard) were 99.85%, 38.89% and 0.61%, respectively. Estimated BTC risk was log-normal pattern, but some of distributions did not have any formal patterns. Except first impact factor(BTC emission quantity), sensitivity analysis showed that main effective factor was retention time in their occupational exposure sites. This case study is a good example to cover that LCA with probabilistic risk analysis tool can supply various significant information such as statistical distribution including personal/environmental exposure level, daily time activity pattern and individual susceptibility. Further research is needed for investigating real data of these input variables and personal exposure concentration and application of this study methodology.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.12
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• pp.1675-1683
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• 2005

In the process of crossbreeding, the linkage disequilibria between the quantitative trait loci (QTL) and their linked markers were reduced gradually with increasing generations. To study the potential of QTL mapping using the crossbred population, we presented a mixed effect model that treated the mean allelic value of the different founder populations as the fixed effect and the allelic deviation from the population mean as random effect. It was assumed that there were fifty QTLs having effect on the trait variation, the population mean and variance were divided to each QTL in founder generation in our model. Only the additive effect was considered in this model for simulation. Six schemes (S1-S6) of crossbreeding were studied. The selection index was used to evaluate the synthetic breeding value of two traits of the individual in the scheme of S2, S4 and S6, and the individuals with high selection index were chosen as the parents of the next generation. Random selection was used in the scheme of S1, S3 and S5. In this study, we premised a QTL explained 40% of the genetic variance was located in a region of 20 cM by the linkage analysis previously. The log likelihood ratio (log LR) was calculated to determine the presence of a QTL at the particular chromosomal position in each of the generations from the fourth to twentieth. The profiles of log LR and the number of the highest log LR located in the region of 5, 10 and 20 cM were compared between different generations and schemes. The profiles and the correct number reduced gradually with the generations increasing in the schemes of S2, S4 and S6, but both of them increased in the schemes of S1, S3 and S5. From the results, we concluded that the crossbreeding population undergoing random selection was suitable for improving the resolution of QTL mapping. Even experiencing index selection, there was still enough variation existing within the crossbred population before the fourteenth generation that could be used to refine the location of QTL in the chromosome region.