• Safety and Health at Work
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• v.13 no.3
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• pp.315-325
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• 2022

Background: Firefighters are required to carry self-contained breathing apparatus (SCBA), which increases the risk of musculoskeletal disorders. This study assessed the newly recruited firefighters' internal forces and potential musculoskeletal disorders when carrying SCBA. The effects of SCBA strap lengths were also evaluated. Methods: Kinematic parameters of twelve male subjects running in a control condition with no SCBA equipped and three varying-strapped SCBAs were measured using 3D inertial motion capture. Subsequently, motion data and predicted ground reaction force were inputted for subject-specific musculoskeletal modeling to estimate joint and muscle forces. Results: The knee was exposed to the highest internal force when carrying SCBA, followed by the rectus femoris and hip, while the shoulder had the lowest force compared to the no-SCBA condition. Our model also revealed that adjusting SCBA straps length was an efficient strategy to influence the force that occurred at the lumbar spine, hip, and knee regions. Grey relation analysis indicated that the deviation of the center of mass, step length, and knee flexion-extension angle could be used as the predictor of musculoskeletal disorders. Conclusion: The finding suggested that the training of the newly recruits focuses on the coordinated movement of muscle and joints in the lower limb. The strap lengths around 98-105 cm were also recommended. The findings are expected to provide injury interventions to enhance the occupational health and safety of the newly recruited firefighters.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.5
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• pp.1-10
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• 2022

For Cu-Cr alloy developed for rocket engines, estimated fatigue lives were calculated using various fatigue life prediction methods and compared with fatigue life acquired from low-cycle fatigue tests. The utilized methods for fatigue life prediction are as follows: Coffin-Manson relation, plastic/total strain energy density relations, Smith-Watson-Topper relation, Tomkins relation, and Jahed-Varvani relation. As results of estimation of fatigue lives, it satisfied within scatter band two compared to the test fatigue lives in all methods. The quantitative calculation of the deviation of predicted fatigue lives gives that the total strain energy density relation presents the best result.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.9
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• pp.1237-1247
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• 2012

The objective of this study was to evaluate the predictions of dry matter intake (DMI) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ${\pm}33.2$ kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination ($r^2$), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision ($r^2$ of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under-or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

• Clean Technology
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• v.26 no.3
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• pp.161-167
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• 2020

For the design of the prevention and mitigation measures in process industries involving flammable substances, reliable safety data are required. An important property used to estimate the risk of fire and explosion for a flammable liquid is the flash point. Flammability is an important factor to consider when developing safe methods for storing and handling solids and liquids. In this study, the flash point data were measured for the binary systems {2-butanol + 2,2,4-trimethylpentane}, {2-butanol + methylcyclohexane} and {2-butanol + toluene} at 101.3 kPa. Experiments were performed according to the standard test method (ASTM D 3278) using a Stanhope-Seta closed cup flash point tester. A minimum flash point behavior was observed in the binary systems as in the many cases for the hydrocarbon and alcohol mixture that were observed. The measured flash points were compared with the predicted values calculated via the following activity coefficient (GE) models: Wilson, Non-Random Two-Liquid (NRTL), and UNIversal QUAsiChemical (UNIQUAC) models. The predicted data were only adequate for the data determined by the closed-cup test method and may not be appropriate for the data obtained from the open-cup test method because of its deviation from the vapor liquid equilibrium. The predicted results of this work can be used to design safe petrochemical processes, such as the identification of safe storage conditions for non-ideal solutions containing flammable components.

• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.1
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• pp.39-47
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• 2010

The officetel introduced into Korea in the mid-1980s has thus far been in the limelight as the object of investment for the substitute for small-sized housing and for lease income. But to raise the possibility of succeeding in officetel investment in the future, it has been necessary to make a systematic analysis of the changed direction of the officetel market and the profitability and stability of investment. Accordingly, this study attempted to analyze demand for officetel with a focus on the possibility of increase in the households composed of one or two members, the major consuming bracket of small-sized housing. And it attempted to analyze the possibility of investment in officetel as the investment goods for lease income due to the entry of Korea into aging society. And past analysis of investment in officetel was confined to profitability analysis, but this study sought to develop the stability indicator of the officetel for the analytical purpose. As a result, it is predicted that demand for small-sized housing will increase due to the increase in 1-to 2-person households. Accordingly, it is predicted that demand for officetel as the place of residence will come to increase. And taking into consideration the more serious degree of sustained aging in the population, older people's preference for real estate and need for lease income, it is predicted that preference for officetel as the object of investment will increase. An attempt was made to analyze the profitability and stability of investment in the metropolitan area which the officetel has principally been supplied, in order to analyze the profitability and stability of officetels. For the purpose of this study, Yeoksam-dong in Kangnam-gu, Yeoui-dong in Youngdeungpo-gu, Bongcheon-dong in Kwanak-gu were selected in Seoul as the area for analysis. Jung-dong in Wonmi-gu, Bucheon, Seohyun-dong in Bundang-gu, Seongnam, Janghang-dong in Ilsan-gu, Koyang were selected in Kyonggi province as the area for analysis. As a result, it was found that the small-sized officetel had higher profitability and stability than the large-sized officetel. It was found that the area of Kyonggi Province had the larger deviation by size. That is, it was found that the small-sized officetel in the area of Kyonggi Province was significant as the object of investment for stable lease income.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.26-31
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• 2023

For durability improvement of polymer electrolyte membrane fuel cell (PEMFC) polymer membrane, accelerated durability evaluation methods that can evaluate durability in a short time have been researched and developed. However, the lifespan of fuel cells for large commercial vehicles such as trucks and buses is more than three times that of passenger cars, and the chemical accelerated stress test (AST) time is also longer, reaching 1,500 hours or more. Therefore, in this study, as a method to evaluate the chemical durability of a membrane within a short time, it was examined whether the durability could be predicted by the pristine membrane characteristics. Hydrogen crossover current density (HCCD) and short resistance (SR) were estimated as initial characteristics, and AST time was predicted through the Fenton experiment, which was possible as an out-of-cell experiment for 3 hours. As the HCCD and fluoride ion emission concentration increased, the AST time tended to be linearly shortened, but there was a deviation (R² ≒0.65). When the SR decreased, the AST time showed a linear increase, and the accuracy was high (R² =0.93), so the AST time could be predicted with the initial SR of the membrane.

• The Journal of Advanced Prosthodontics
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• v.15 no.6
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• pp.281-289
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• 2023

PURPOSE. This study aimed to predict the positional coordinates of incisor points from the scan data of conventional complete dentures and verify their accuracy. MATERIALS AND METHODS. The standard triangulated language (STL) data of the scanned 100 pairs of complete upper and lower dentures were imported into the computer-aided design software from which the position coordinates of the points corresponding to each landmark of the jaw were obtained. The x, y, and z coordinates of the incisor point (X_P, Y_P, and Z_P) were obtained from the maxillary and mandibular landmark coordinates using regression or calculation formulas, and the accuracy was verified to determine the deviation between the measured and predicted coordinate values. Y_P was obtained in two ways using the hamularincisive-papilla plane (HIP) and facial measurements. Multiple regression analysis was used to predict Z_P. The root mean squared error (RMSE) values were used to verify the accuracy of the X_P and Y_P. The RMSE value was obtained after crossvalidation using the remaining 30 cases of denture STL data to verify the accuracy of Z_P. RESULTS. The RMSE was 2.22 for predicting X_P. When predicting Y_P, the RMSE of the method using the HIP plane and facial measurements was 3.18 and 0.73, respectively. Cross-validation revealed the RMSE to be 1.53. CONCLUSION. Y_P and Z_P could be predicted from anatomical landmarks of the maxillary and mandibular edentulous jaw, suggesting that Y_P could be predicted with better accuracy with the addition of the position of the lower border of the upper lip.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3543-3548
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• 2023

Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.