• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.4
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• pp.393-400
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• 2011

In this study, a procedure for the inverse estimation of the fatigue life parameters of springs which utilize the field fatigue life test data is proposed to replace real test with the FEA on fatigue life prediction. The Bayesian approach is employed, in which the posterior distributions of the parameters are determined conditional on the accumulated life data that are routinely obtained from the regular tests. In order to obtain the accurate samples from the distributions, the Markov chain Monte Carlo (MCMC) technique is employed. The distributions of the parameters are used in the FEA for predicting the fatigue life in the form of a predictive interval. The results show that the actual fatigue life data are found well within the posterior predictive distributions.

• Food Science and Biotechnology
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• v.14 no.1
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• pp.42-45
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• 2005

The present study was carried out to envisage the aerobic growth of Listeria monocytogenes and Yersinia enterocolitica on pork, which is one of the major meat sources in Korea. The results were compared with the previously developed predictive model systems for the verification of microbial growth in a real situation during pork processing. Pork loin samples (8.0 g, 5 mm thick) were aseptically prepared and inoculated with each pathogen by immersing into the respective inoculums for one min. Each of the samples were then wrapped with PE film and stored at 5, 10, and $15^{\circ}C$ up to 36 days to measure the growth profile of the respective pathogens. The growth parameters were calculated by using Gompertz equation and were compared with the previously reported data. The predicted generation time (GT) of L. monocytogenes at 5, 10 and $15^{\circ}C$ was 28.74, 7.85 and 4.02 hr, respectively, and for Y. enterocolitica was 10.29, 4.74 and 2.50 hr, at the same temperatures respectively. In this study, the GT values predicted on pork were slightly higher than the values predicted in other studies using liquid model systems. Unlike previous reports, both the pathogens were found to grow at $5^{\circ}C$ on pork. This finding recommends the necessity of controlling the growth of both the pathogens during the slaughtering process and distribution.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.369-378
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• 2012

A load-following operation in APR+ nuclear plants is necessary to reduce the need to adjust the boric acid concentration and to efficiently control the control rods for flexible operation. In particular, a disproportion in the axial flux distribution, which is normally caused by a load-following operation in a reactor core, causes xenon oscillation because the absorption cross-section of xenon is extremely large and its effects in a reactor are delayed by the iodine precursor. A model predictive control (MPC) method was used to design an automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control for APR+ nuclear plants. Some tracking controllers employ the current tracking command only. On the other hand, the MPC can achieve better tracking performance because it considers future commands in addition to the current tracking command. The basic concept of the MPC is to solve an optimization problem for generating finite future control inputs at the current time and to implement as the current control input only the first control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The support vector regression (SVR) model that is used widely for function approximation problems is used to predict the future outputs based on previous inputs and outputs. In addition, a genetic algorithm is employed to minimize the objective function of a MPC control algorithm with multiple constraints. The power level and ASI are controlled by regulating the control banks and part-strength control banks together with an automatic adjustment of the boric acid concentration. The 3-dimensional MASTER code, which models APR+ nuclear plants, is interfaced to the proposed controller to confirm the performance of the controlling reactor power level and ASI. Numerical simulations showed that the proposed controller exhibits very fast tracking responses.

• The Journal of Advanced Prosthodontics
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• v.12 no.2
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• pp.49-54
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• 2020

PURPOSE. The purpose of this study was to compare the performance of Accura to that of the T-scan for indicating occlusal contacts. MATERIALS AND METHODS. Twenty-four subjects were selected. Their maxillary dental casts were scanned with a model scanner. The Stereolithography files of the casts were positioned to align with the occlusal plane. Occlusal surfaces of every tooth were divided into three to six anatomic regions. T-scan and Accura recordings were made during two masticatory cycles. The T-scan and Accura images were captured at the maximum bite force and overlapped to the cast. Photographs of interocclusal records were used as the reference during overlap. The occlusal contacts were counted to compare the T-scan and Accura. McNemar's test was used for statistical significance and the corresponding P-values were calculated from a chi-square distribution with one degree of freedom. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Accura were calculated relative to the T-scan values as a control. RESULTS. No statistical differences (P>.05) were found between the T-scan and Accura methods. The accuracy of Accura was 75.8%, sensitivity was 82.1%, specificity was 60.1%, PPV was 82.9%, and NPV was 60.1%. CONCLUSION. Accura could be another possible option as a computerized occlusal analysis system for indicating occlusal contacts at maximum intercuspation.

• Journal of Embryo Transfer
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• v.32 no.3
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• pp.183-192
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• 2017

The mammary gland tumor (MGT) is the most common neoplasia in intact female dogs. Of these, 50% are malignant and metastasis to the other sites are often occurred. Therefore, it is very important for decision of treatment plan and prognosis to differentiate benign tumor from malignancies. Calcification of MGT is a very important imaging finding. The purpose of this study was to investigate the radiological and computed tomographic images of the MGT and the morphology and distribution of calcifications in the MGT using the Breast Imaging Reporting and Data System classification. A total of 42 dogs with MGT were included in this study. The dogs were divided into two groups into benign and malignant groups based upon histologic or cytologic results. The appearance of calcification in the tumor on radiographs and CT images was analyzed for the HU value of pre- and post-contrast injection, margin, surface, and shape of the tumor and the lymph node abnormalities. On radiographs, the positive predictive value of malignant and benign tumors was 72.72 and 85.71%, respectively. On CT examinations, the positive predictive value of malignant and benign tumors was the same value of 83.33%. The maximum diameter of the tumor and the presence of abnormal lymph nodes on CT images showed a strong correlation with malignancies. Therefore, it is thought that radiographs and CT provide useful information for evaluating MGT in dogs.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.18 no.3
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• pp.1-6
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• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.141-150
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• 2021

Fine dust is a substance that greatly affects human health, and various studies have been conducted in this regard. Due to the human influence of particulate matter, various studies are being conducted to predict particulate matter grade using past data measured in the monitoring network of Seoul city. In this paper, predictive model have focused on particulate matter concentration in May, 2019, Seoul. The air pollutant variables were used to training such as SO₂, CO, NO₂, O₃. The predictive model based on Adaboost, and training model was dividing PM₁₀ and PM_2.5. As a result of the prediction performance comparison through confusion matrix, the Adaboost model was more conformable for predicting the particulate matter concentration grade. Although air pollutant variables have a higher correlation with PM2.5, training model need to train a lot of data and to use additional variables such as traffic volume to predict more effective PM₁₀ and PM_2.5 distribution grade.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of Animal Science and Technology
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• v.65 no.5
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• pp.1024-1039
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• 2023

In this study, we performed a quantitative microbial risk assessment (QMRA) of Salmonella through intake of egg consumption after cooking (dry-heat, moist-heat, and raw consumption). Egg samples (n = 201) from retail markets were analyzed for the presence of Salmonella. In addition, temperature and time were investigated during egg transit, storage, and display. A predictive model was developed to characterize the kinetic behavior of Salmonella in eggs, and data on egg consumption and frequency were collected. Eventually, the data was simulated to estimate egg-related foodborne illnesses. Salmonella was not found in any of the 201 egg samples. Thus, the estimated initial contamination level was -4.0 Log CFU/g. With R² values of 0.898 and 0.922, the constructed predictive models were adequate for describing the fate of Salmonella in eggs throughout distribution and storage. Eggs were consumed raw (1.5%, 39.2 g), dry-heated (57.5%, 43.0 g), and moist-heated (41%, 36.1 g). The probability of foodborne Salmonella illness from the consumption of cooked eggs was evaluated to be 6.8×10^-10. Additionally, the probability of foodborne illness not applied cooking methods was 1.9×10^-7, indicating that Salmonella can be reduced by cooking. Therefore, the risk of Salmonella infection through consumption of eggs after cooking might be low in S. Korea.

• Water Engineering Research
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• v.1 no.2
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• pp.137-146
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• 2000

A windows-based software package, named DREDGE, is developed for estimating sediment resuspension and contaminant release during dredging operations. DREDGE allows user to enter the necessary dredge information, site characteristics, operational data, and contaminant characteristics, then calculates an array of concentration using the given values. The program mainly consists of the near-field models, which are obtained empirically, for estimating sediment resuspension and the far-field models, which are obtained analytically, for suspended sediment transport. A linear equilibrium partitioning approach is applied to estimate particulate and dissolved contaminant concentrations. This software package which requires only a minimal amount of data consists of three components; user input, tabular output, and graphical output. Combining the near-field and far-field models into a user-friendly windows-based computer program can greatly save dredge operator's, planners', and regulators' efforts for estimating sediment transports and contaminant distribution.