• 한국식품과학회지
• /
• 제25권5호
• /
• pp.481-486
• /
• 1993

본 연구는 유화형 소시지 제조시 전보에서 분석된 원료육의 성분과 기능성 자료를 데이타베이스로 하여, 컴퓨터를 이용한 최소가격배합을 자성하여 그 적용의 실례를 들고 제한조건의 하나로 설정된, 결착지수의 부합도를 실제 제품제조를 통해 검토한 후 적정 결착지수 제한조건을 찾기 위하여 실시하였다. 결착지수 제한조건을 달리하여 최소가격배합비로 제조한 프랑크푸르터소시지의 실측 결착성을 측정한 결과 예측치와 일치하지는 않았으나 비례하여 나타났으며, 일치하지 않은 이유는 결착지수가 모형시스템에 의해서 구해진 관계로 실제 제조공정과 조건이 다른 것으로 사려된다. 또한 조직감 분석대상들을 결착지수와 비교했을 때 소시지의 조직감을 표현하는 바람직한 지표로서 cohesiveness와 함께 hardness를 동시에 고려할 필요가 있었다. 시중 제품의 결착성과 경도를 고려하여 볼 때 본 연구의 결착지수 제한조건은 $0.16{\sim}0.17$로 설정될 수 있었다.

• Journal of Obesity & Metabolic Syndrome
• /
• 제27권4호
• /
• pp.238-247
• /
• 2018

Background: We aimed to estimate the proportion of patients with diabetes who achieved target glycemic control, to estimate diabetes-related costs attributable to poor control, and to identify factors associated with them in the United Arab Emirates. Methods: This retrospective cohort study used administrative claims data handled by Abu Dhabi Health Authority (January 2010 to June 2012) to determine glycemic control and diabetes-related treatment costs. A total of 4,058 patients were matched using propensity scores to eliminate selection bias between patients with glycosylated hemoglobin (HbA1c) <7% and HbA1c ${\geq}7%$. Diabetes-related costs attributable to poor control were estimated using a recycled prediction method. Factors associated with glycemic control were investigated using logistic regression and factors associated with these costs were identified using a generalized linear model. Results: During the 1-year follow-up period, 46.6% of the patients achieved HbA1c <7%. Older age, female sex, better insurance coverage, non-use of insulin in the index diagnosis month, and non-use of antidiabetic medications during the follow-up period were significantly associated with improved glycemic control. The mean diabetes-related annual costs were $2,282 and $2,667 for patients with and without glycemic control, respectively, and the cost attributable to poor glycemic control was $172 (95% confidence interval [CI], $164-180). The diabetes-related costs were lower with mean HbA1c levels <7% (cost ratio, 0.94; 95% CI, 0.88-0.99). The costs were significantly higher in patients aged ${\geq}65$ years than those aged ${\leq}44$ years (cost ratio, 1.45; 95% CI, 1.25-1.70). Conclusion: More than 50% of patients with diabetes had poorly controlled HbA1c. Poor glycemic control may increase diabetes-related costs.

• International Journal of Precision Engineering and Manufacturing
• /
• 제8권3호
• /
• pp.64-70
• /
• 2007

Of all the rapid tooling (RT) methods currently available, thick-layer laminated tooling is the most suitable for large-scale, low-cost dies and molds. Currently, the determination of a lamina's contour or profile and the associated slicing algorithms are based on existing rapid prototyping (RP) data manipulation technology. This paper presents a new adaptive slicing algorithm developed exclusively for profiled edge laminae (PEL) tooling PEL tooling is a thick-layer RT technique that involves the assembly of an array of laminae, whose top edges are simultaneously profiled and beveled using a line-of-sight cutting method based on a CAD model of the intended tool surface. The cutting profiles are based on the intersection curve obtained directly from the CAD model to ensure geometrical accuracy. The slicing algorithm determines the lamina thicknesses that minimize the dimensional error using a new tool shape error index. At the same time, the algorithm considers the available lamination thicknesses and desired lamina interface locations. We demonstrate the new slicing algorithm by developing a simple industrial PEL tool based on a CAD part shape.

• 디지털융복합연구
• /
• 제9권5호
• /
• pp.141-156
• /
• 2011

정보시스템 감리에서 발생하는 감리인의 주관적인 판단은 감리 의견에 대한 신뢰성을 저하시키는 요인이 되고 있다. 본 논문은 정보시스템 감리에서 요구사항의 중요성과 감리 평가에 대한 객관성의 향상을 위하여 요구사항 우선순위를 통한 감리 정량화 모델을 제시하였다. 요구사항의 가치는 동일한 레벨에 있지 않다는 전제하에 고객 요구사항 기준으로 평가하였다. 또한 요구사항 우선순위 기준에 대한 세부적인 평가 지표를 발주자 측면 요소인 서비스 중요도와 기능 만족도, 사업자 측면 요소인 비용과 일정으로 세분화하여 객관적인 감리 수행 결과의 정량화를 가능하게 하여 감리의 객관성을 향상시키도록 하였다.

• Nuclear Engineering and Technology
• /
• 제54권3호
• /
• pp.940-947
• /
• 2022

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional-integral-derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

• Geomechanics and Engineering
• /
• 재33권6호
• /
• 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.

• Medical Lasers
• /
• 제9권1호
• /
• pp.51-57
• /
• 2020

Background and Objectives For experiments on simulated burn, the preparation of an animal model is a very important step. The purpose of the current experiment is to design a simple and controllable method for the preparation of third-degree scald in a mouse model using the boiling water method. Materials and Methods A total of 18 Swiss mice were used. After the anesthetization, the mice were scalded by boiling water (100℃) using a mold with a 1 cm² circle area on the dorsum at contact times of 3s, 5s, and 8s. After confirming that 8 seconds of scald can cause a third-degree scald, the skin samples were collected at day 2, 4, 6, 8, 10, and 12, and analyzed by histopathological examinations. The wound retraction index (WRI) was also measured. Results Third-degree scald involving full-thickness skin was observed in the 8-second scald group, while a 3-second scald caused a superficial second-degree scald and a 5-second scald caused a deep second-degree scald. After third-degree scald, the burn wound continued to contract until day 14. Conclusion The scalding model of mice can be successfully established by the boiling water method. This method is easy to operate, it has a low cost, and it can control the scald depth by controlling the scald time. This is adequate to study skin thermal injury in the future. The scald model established by this method can last for 14 days.

• Clinical Nutrition Research
• /
• 제12권2호
• /
• pp.138-153
• /
• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Steel and Composite Structures
• /
• 제52권2호
• /
• pp.145-163
• /
• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• 유통과학연구
• /
• 제11권8호
• /
• pp.5-14
• /
• 2013

Purpose - We live in a world of constant change and competition. Many airports have specific competitiveness goals and strategies for achieving and maintaining them. The global economic recession, financial crises, and rising oil prices have resulted in an increasingly important role for facility investment and renewal and the implementation of appropriate policies in ensuring the competitive advantage for airports. It is thus important to analyze the factors that enhance efficiency and productivity for an airport. This study aims to determine the efficiency levels of 20 major airports in East Asia, Europe, and North America. Further, this study also suggests suitable policies and strategies for their development. Research design, data, and methodology - This paper employs the DEA-CCR, DEA-BCC, and DEA-Malmquist production index analysis models to determine airport efficiency. The study uses data on the efficiency and productivity of the world's leading airports between 2006 and 2010. The input variables include the airport size, the number of runways, the size of passenger terminals, and the size of cargo terminals. The output variables include the annual number of passengers and the annual cargo volume. The study uses basic data from the 2010 World Airport Traffic Report (ACI). The world's top 20 airports (as rated by the ACI report) are investigated. The study uses the expanded DEA Model and the Super Efficiency Model to identify the most effective airports among the top 20. The Malmquist productivity index analysis is used to measure airport effectiveness. Results - This study analyzes longitudinal and cross-sectional data on the world's top 20 airports covering 2006 to 2010. A CCR analysis shows that the most efficient airports in 2010 were Gatwick Airport (LGW), Zurich Airport (ZRH), Vienna Airport (VIE), Leonardo da Vinci Fiumicino Airport (FCO), Los Angeles International Airport (LAX), Seattle-Tacoma Airport (SEA), San Francisco Airport (SFO), HongKong Airport (HKG), Beijing Capital International Airport (PEK), and Shanghai Pudong Airport (PVG). We find that changes in airport productivity are affected more by technical factors than by airport efficiency. Conclusions - Based on the study results, we offer four airport development proposals. First, a benchmark airport needs to be identified. Second, inefficiency must be reduced and high-cost factors need to be managed. Third, airport operations should be enhanced through technical innovation. Finally, scientific demand forecasting and facility preparation must become the focus of attention. This paper has some limitations. Because the Malmquist productivity index is based on the hypothesis of the, the identified production change could be over- or under-estimated. Further, as DEA estimates the relative efficiency. It also cannot generalize to include all airport conditions because the variables are limited. To measure airport productivity more accurately, other input variables and environmental variables such as financial and policy factors should be included.