• Steel and Composite Structures
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• 제18권3호
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• The Korean Journal of Pain
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• 제24권1호
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• pp.22-30
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• 2011

Background: Pregabalin has been shown to have analgesic effect in acute pain models. The primary objective was to examine the efficacy a single dose of pregabalin, would have on morphine consumption following lumbar discectomy. Methods: With ethical approval a randomized, placebo-controlled prospective trial was undertaken in 32 patients (ASA I-II, 18-65 years) with radicular low back pain for > 3 months undergoing elective lumbar discectomy. Patients received either oral pregabalin 300 mg (PG Group) or placebo (C Group) one hour before surgery. Pain intensity, the accumulative morphine consumption and adverse effects were recorded for 24 hours following surgery. Functional, psychological and quantitative sensory testing were also assessed. Results: Fourteen patients out of the 32 recruited were randomized to receive pregabalin. Morphine consumption was reduced (absolute difference of 42.3%) between groups with medium effect size. (Mann-Whitney; U =52.5, z-score= 2.84, P = 0.004, r = 0.14). This was not associated with a significant difference in the incidence of adverse effects between the two groups. The median pain intensity (VAS) on movement was not significantly different between groups. Conclusions: A single pre-operative dose of pregabalin (300 mg) did not result in a reduction in pain intensity compared to placebo in this patient cohort but the significant reduction in morphine consumption suggests that a fixed peri-operative dosing regime warrants investigation.

• 한국의류학회지
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• 제27권5호
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• pp.463-474
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• 2003

In this research, adult women＇s shoulder types were Classified through direct and indirect measurements to present a judging individual body size according to the type. Also, regression formula by shoulder types were calculated and presented the basic bodice pattern. The results were as follows: 1. The result of factor analysis indicated that 6 factors were extracted through factor analysis and those factors comprised 66.1 to of total variance. 2. By using factor scores, cluster analysis was carried out and subject were classified into 5 clusters. Type 1 was the inclined shoulders, wide shoulders and passive posture. Type 2 was the front type shoulders and active posture. Type 3 was the thick shoulders and back type shoulders. Type 4 was the narrow shoulders. Type f was the drooped shoulders, thin shoulder and sway posture. 3. The body types of individuals were judged by discriminant analysis. 4. After setting 4 items such as the bust girth, posterior waist length, neck base girth and waist girth as representative items and regression formulas were presented. the superiority of the final basic bodice patterns were demonstrated by high approval rate of the subjects who participated in testing.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.801-807
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• 2010

Requirements management is one of the most essential activities of systems engineering in developing successful weapon systems. Particularly it is very important to consistently manage the traceability among the user requirements, the system requirements, the development specifications and the testing plans throughout the entire life cycle of the weapon system. However, the most part of requirements-related activities has centered around the functional and performance requirements but the integrated logistics support(ILS) requirement has not properly been managed. In this regard, a special attention is needed to develop and manage the ILS requirements. To do so, the ANSI/EIA-632 standard can be referred as a starting point since the ILS requirements of the weapon system under development are specified by the enabling products whereas the functional and performance requirements are covered by the end product requirements. Specifically, we first review and model several cases of previous weapon systems development, which reveals the problem of interest. Then, under the framework of ANSI/EIA-632, we study a hierarchical model for effectively managing ILS requirements by analyzing the features of ILS requirements. Finally, the value of the proposed model is discussed through the case study of electronic warfare equipment.

• Computers and Concrete
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• 제21권6호
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of the Optical Society of Korea
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• 제17권4호
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• pp.323-327
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• 2013

A laser ultrasonic inspection system has the advantage of nondestructive testing. It is a non-contact mode using a laser interferometer to measure the vertical displacement of the surface of a material caused by the propagation of ultrasonic signals with the remote ultrasonic generated by laser. After raising the ultrasonic signal with a broadband frequency range using a pulsed laser beam, the laser beam is focused to a small point to measure the ultrasonic signal because it provides an excellent measurement resolution. In this paper, foreign materials are measured by a non-destructive and non-contact method using the laser ultrasonic inspection system. Mixed foreign material on the corroded part is assumed and the laser ultrasonic experiment is conducted. An ultrasonic wave is generated by pulse laser from the back of the specimen and an ultrasonic signal is acquired from the same location of the front side using continuous wave laser and Confocal Fabry-Perot Interferometer (CFPI). The characteristic of the ultrasonic signal of existing foreign material is analyzed and the location and size of foreign material is measured.

• Geosystem Engineering
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• 제21권5호
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• pp.273-281
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• 2018

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value ($R^2$) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제25권1호
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• pp.13-20
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• 2022

Purpose: Hydrogen-methane breath tests are used to diagnose carbohydrate malabsorption and small intestinal bacterial overgrowth. The COVID-19 pandemic has driven the modification of procedures as breath tests are potentially aerosol-generating procedures. We assessed the effect of delayed analysis of breath samples, facilitating the at-home performance of breath testing. Methods: Children provided two breath samples at every step of the lactose breath test. The samples were brought back to the clinic, and one set of samples was analyzed immediately. The second set was stored at room temperature and analyzed 1-4 days later. Results: Out of the 73 "double" lactose breath tests performed at home, 33 (45.8%) were positive. The second samples were analyzed 20 to 117 hours after the first samples (41.7±24.3 hours). There was no significant difference in the hydrogen concentration between the first and second sets (Z=0.49, p=0.62). This was not the case for methane, which had a significantly higher concentration in the second breath samples (Z=7.6). Conclusion: Expired hydrogen levels remain stable in plastic syringes if preserved at room temperature for several days. On the other hand, the delayed analysis of methane appeared to be less reliable. Further research is needed to examine the impact of delayed analysis on methane and hydrogen concentrations.

• Current Optics and Photonics
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• 제8권3호
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• 한국지반공학회논문집
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• 제20권8호
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• pp.77-87
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• 2004

본 논문에서는 국내 서남해안 11개 지역에서 수행된 63회의 피에조콘 시험결과와 176개의 선행압밀하중 자료로부터 국내 연약지반의 선행압밀하중 예측을 위한 오차 역전파 알고리즘으로 학습된 피에조콘 인공신경망 모델을 구축하였다. 전체 자료 중 147개의 자료만이 인공신경망 모델 구축을 위한 학습과정에 사용되었으며 학습에 사용되지 않은 29개의 자료를 구축된 인공신경망의 검증에 활용하였다. 또한 기존의 경험모델 및 이론모델과 비교하여 제안된 인공신경망 모델의 유용성을 확인하였다. 연구를 통하여 4-4-9-1의 구조를 갖는 간단한 다층 인공신경망이 구축되었으며 입력값으로는 피에조콘 선단저항력 $q_T$, 관입간극수압 $u_2$그리고 지반의 총상재하중 $\sigma_{vo}$ 및 유효상재하중 $\sigma'_{vo}$ 이 사용되었다. 제안된 인공신경망 모델은 학습되지 않은 새로운 검증자료에 대한 예측을 통하여 입력변수들과 선행압밀 하중 간의 비선형적 상관관계를 성공적으로 모델하는 것으로 검증되었으며 정확성면에서는 기존의 이론모델과 국내외 경험모델과 비교할 때 월등히 향상된 예측능력을 가진 것으로 나타났다. 뿐만 아니라 제안된 모델은 국내 특정지 역에 대한 모델이 아니라 서남해안의 다양한 지반특성을 갖는 지반에서 수행된 자료를 바탕으로 구축되어 데이터베이스에 포함되지 않은 지역에 대하여도 매우 타당성있는 예측결과를 주어 특정지역에 국한된 지역의존적 예측이 아닌 일반화된 지역에서 적용할 수 있을 것으로 판단된다.