• Journal of the Korean Society of Mechanical Technology
• /
• v.20 no.6
• /
• pp.844-851
• /
• 2018

In this study, the regression equation was suggested to predict of the shot ball velocity according to blade shapes based on discrete element (DE) analysis. First, the flat type blade DE model was used in the analysis, the validity of the DE model was verified by giving that the velocity of the shot ball almost equal to the theoretical one. Next, the DE analyses for curved and combined blade models was accomplished, and their analytical velocities of shot ball were compared with the theoretical one. The velocity of combined blade model was greatest. From this, the regression equation for velocity of shot ball according to the blade shape based on the DE analysis was derived. Additionally, the wind speed measurement experiment was carried out, and the experimental result and analytical one were the same. Ultimately, it was confirmed that the prediction method of the velocity of shot ball based on DE analysis was effective.

• Transactions of Materials Processing
• /
• v.31 no.5
• /
• pp.296-301
• /
• 2022

In this study, influence of the process parameters of the roller leveling process on the straightness of the steel wire was analyzed using numerical analysis. To construct the numerical analysis model, cross-sectional and longitudinal element sizes, which affect the prediction accuracy of longitudinal stress caused by bending deformation of the steel wire, were optimized, and mass scaling that satisfies prediction accuracy while reducing computational time was confirmed. By using the constructed numerical analysis model, the influence of various process parameters such as input direction of the steel wire, initial diameter of the steel wire, back tension and intermesh on the straightness was confirmed. The simulation result shows that the 3^rd and 4^th roller of vertical straightener had a significant influence on vertical shape of the steel wire.

• Geomechanics and Engineering
• /
• v.33 no.6
• /
• pp.625-633
• /
• 2023

The volume changes associated with moisture or suction variation in expansive soils are of geotechnical and geoenvironmental design concern. These changes can impact the performance of infrastructure projects and lightweight structures. Assessment of unsaturated function for these materials leads to better interpretation and understanding, as well as providing accurate and economic design. In this study, expansive soils from different regions of Saudi Arabia were studied for their basic properties including gradation, plasticity and shrinkage, swelling, and consolidation characteristics. The unsaturated soil functions of saturated water content, air-entry values, and residual states were determined by conducting the tests for the entire soil water characteristic curves (SWCC) using different techniques. An attempt has been made to provide a prediction model for unsaturated properties based on the basic properties of these soils. Once the profile of SWCC has been predicted the time and cost for many tests can be saved. These predictions can be utilized in practice for the application of unsaturated soil mechanics on geotechnical and geoenvironmental projects.

• Korean Chemical Engineering Research
• /
• v.54 no.5
• /
• pp.621-629
• /
• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• Journal of Climate Change Research
• /
• v.6 no.4
• /
• pp.319-330
• /
• 2015

This study was carried out to predict the optimal growth site and estimate carbon stocks of Quercus acuta, evergreen broad-leaved trees in warm temperate zone according to climate change. The criterion for the optimal site prediction was created by quantification method with quantitative and qualitative data, collected from growth factors of stands and environmental factors of survey sites of 42 plots in Q. acuta by study relationship between growth of tree and site environmental factors. A program for the optimal site prediction was developed by using GIS engine tools. To prediction of the suitable growth site of Quercus acuta, developed program in this study applied to Wando in Jeollanam-do, distributing a various evergreen bread-leaved trees of warm temperate zone. In the results from analysis of the optimal site prediction on Q. acuta, the characteristics of the optimal site showed as follows; site environmental features of class I (the best site class for Q. acuta) was defined as 401 ~ 500 m of altitude, $21{\sim}25^{\circ}$ of slope with above hillside, residual of deposit convex of slope type with west of aspect. The area and carbon stocks of optimal site prediction by class for Q. acuta in classI showed 147.1 ha (2.5%), total 316.5 tC/ha, total $1,161tCO_2/ha/yr$ of class I, 2,703.5 ha (46.3%), total 5,817.4 tC/ha, total $21,331tCO_2/ha/yr$ of class II, 2,845.5 ha (48.6%), total 6,123.0 tC/ha, total $2,845.5tCO_2/ha/yr$ of class III and 153.7 ha (2.6%), total 330.7 tC/ha, total $1,213.7tCO_2/ha/yr$ of class IV.

• Agricultural and Biosystems Engineering
• /
• v.7 no.1
• /
• pp.1-7
• /
• 2006

A need exists to monitor and control the localized high temperatures often experienced in solar grain dryers, which result in grain cracking, reduced germination and loss of cooking quality. A verified finite element model would be a useful to monitor and control the drying process. This study examined the feasibility of the finite element method (FEM) to predict temperature distribution in solar grain dryers. To achieve this, an indirect solar grain dryer system was developed. It consisted of a solar collector, plenum and drying chambers, and an electric fan. The system was used to acquire the necessary input and output data for the finite element model. The input data comprised ambient and plenum chamber temperatures, prevailing wind velocities, thermal conductivities of air, grain and dryer wall, and node locations in the xy-plane. The outputs were temperature at the different nodes, and these were compared with measured values. The ${\pm}5%$ residual error interval employed in the analysis yielded an overall prediction performance level of 83.3% for temperature distribution in the dryer. Satisfactory prediction levels were also attained for the lateral (61.5-96.2%) and vertical (73.1-92.3%) directions of grain drying. These results demonstrate that it is feasible to use a two-dimensional (2-D) finite element model to predict temperature distribution in a grain solar dryer. Consequently, the method offers considerable advantage over experimental approaches as it reduces time requirements and the need for expensive measuring equipment, and it also yields relatively accurate results.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.7 no.2
• /
• pp.101-111
• /
• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.8C
• /
• pp.822-829
• /
• 2009

This paper proposes a new method of improving the quality of High Efficiency Advanced Audio Coding (HE-AAC). HE-AAC encodes input source by allocating bits for each scalefactor bands appropriately according to human ear's psychoacoustic property. As a result, insufficient bits are assigned to the bands which have relatively low energy. This imbalance between different energy bands can cause decreasing of sound quality like musical noise. In the proposed system, a Linear Prediction (LP) module is combined with HE-AAC as a pre-processor to improve sound quality by even bits distribution. To apply accurate human being's psychoacoustic property, the psychoacoustic model uses Fast Fourier Transform (FFT) spectrum of original input signal to make masking threshold. In its implementation, masking threshold of psychoacoustic model is normalized using the LP spectral envelope in prior to quantization of the LP residual. Experimental result shows that, the proposed algorithm allocates bits appropriately for insufficient bits condition and improves the performance of HE-AAC.

• Steel and Composite Structures
• /
• v.36 no.1
• /
• pp.75-86
• /
• 2020

The grid structures with welded hollow spherical joint (WHSJ) have gained increasing popularity for use in industrial buildings with suspended cranes, and usually welded with steel tube (ST). The fatigue performance of steel tube-welded hollow spherical joint (ST-WHSJ) is however not yet well characterized, and there is little research on fatigue life prediction methods of ST-WHSJ. In this study, based on previous fatigue tests, three series of specimen fatigue data with different design parameters and stress ratios were compared, and two fatigue failure modes were revealed: failure at the weld toe of the ST and the WHSJ respectively. Then, S-N curves of nominal stress were uniformed. Furthermore, a finite element model (FEM) was validated by static test, and was introduced to assess fatigue behavior with the hot spot stress method (HSSM) and the effective notch stress method (ENSM). Both methods could provide conservative predictions, and these two methods had similar results. However, ENSM, especially when using von Mises stress, had a better fit for the series with a non- positive stress ratio. After including the welding residual stress and mean stress, analyses with the local stress method (LSM) and the critical distance method (CDM, including point method and line method) were carried out. It could be seen that the point method of CDM led to more accurate predictions than LSM, and was recommended for series with positive stress ratios.

• Korean Journal of Radiology
• /
• v.22 no.9
• /
• pp.1481-1489
• /
• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.