• Safety and Health at Work
• /
• 제13권3호
• /
• pp.315-325
• /
• 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.

• 한국추진공학회지
• /
• 제26권5호
• /
• pp.1-10
• /
• 2022

로켓엔진용으로 개발된 구리크롬 합금에 대해 저주기 피로시험을 수행하고 여러가지 피로수명 예측방법을 이용해 예측수명을 구하여 이를 시험수명과 비교하여 보았다. 피로수명 예측방법으로는 Coffin-Manson 관계식, 소성 및 전 변형률 에너지 밀도 관계식, Smith-Watson-Topper 관계식, Tomkins 관계식, Jahed-Varvani 관계식 등 총 6가지 방법을 이용하였다. 피로 예측수명을 계산한 결과 모든 방법에서 시험수명 대비 분산범위 2 이내를 만족하였다. 예측수명 편차의 정량적 확인을 통해 전 변형률 에너지 밀도 관계식이 가장 우수한 결과를 나타냈다.

• Geomechanics and Engineering
• /
• 제30권6호
• /
• pp.551-564
• /
• 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.

• 자동차안전학회지
• /
• 제15권1호
• /
• pp.55-62
• /
• 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
• /
• 제25권9호
• /
• pp.1237-1247
• /
• 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.

• 청정기술
• /
• 제26권3호
• /
• pp.161-167
• /
• 2020

가연성 물질을 사용하는 화학공정 산업에서 저장 안전성을 높이고 화재 및 폭발 예방 조치를 설계하려면 신뢰할 수 있는 인화점에 대한 정보가 필요하다. 본 연구는 석유화학 공정에서 중요한 용매와 가솔린의 옥탄가 향상제로 사용되는 방향족, 나프텐 및 파라핀계 탄화수소 화합물과 알킬알코올에 대한 이성분 혼합물의 인화점 데이터를 얻는 것이다. 그래서 이성분 혼합물인 {2-butanol + 2,2,4-trimethylpentane}, {2-butanol + methylcyclohexane} 그리고 {2-butanol + toluene} 계에 대한 최소인화점을 Stanhope-Seta 밀폐식 인화점 측정기를 이용하여 측정하였다. 각 이성분계 혼합물에 대한 인화점을 예측하기 위해 이상성인 라울의 법칙(Raoult's law)과 비이상성인 Wilson, NRTL 그리고 UNIQUAC 매개변수를 이용하였고 실험 결과와 비교해 보았다. 이상성을 나타내는 라울의 법칙(Raoult's law)보다 비이상 용액 혼합물의 활동도 계수 모델에서 2.36 K 이하의 좋은 결과를 나타내었다. 본 연구의 결과는 가연성 혼합물을 함유한 석유화학 용매의 안전한 저장 및 공정 설계에 적용할 수 있다.

• 한국디지털건축인테리어학회논문집
• /
• 제10권1호
• /
• pp.39-47
• /
• 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
• /
• 제61권1호
• /
• pp.26-31
• /
• 2023

고분자 전해질 연료전지(Polymer electrolyte membrane fuel cell, PEMFC) 고분자막의 내구성 향상을 위해서 빠른 시간에 내구성을 평가할 수 있는 가속 내구 평가법들이 연구 개발되었다. 그러나 트럭, 버스 등 대형 상용차용 연료전지 수명은 승용차보다 3배 이상 요구되어 화학적 가속 내구 평가(Accelerated stress test, AST) 시간도 길어져서 1,500 시간 이상이 되었다. 그래서 본 연구에서는 단 시간내에 고분자막의 화학적 내구성을 평가하기 위한 방법으로 막 초기 특성으로 내구성을 예측할 수 있을지 검토하였다. 초기 특성으로 수소투과전류밀도(Hydrogen crossover current density, HCCD)와 단락 저항(Short resistance, SR)을 그리고 3시간의 셀 밖 실험으로 가능한 Fenton 실험을 통해 AST 시간을 예측하였다. HCCD와 불소 이온 유출 농도가 증가하면 AST 시간이 선형적으로 짧아지는 경향을 보였으나 편차가 있었다(R² ≒0.65). SR이 감소하면 AST 시간이 선형적으로 증가하는 상관관계를 보였으며 정확도가 높아(R² =0.93) 고분 자막 초기 SR로 AST 시간을 예측할 수 있었다.

• The Journal of Advanced Prosthodontics
• /
• 제15권6호
• /
• pp.281-289
• /
• 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
• /
• 제55권10호
• /
• pp.3543-3548
• /
• 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.