• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2010년도 제35회 추계학술대회논문집
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• pp.499-505
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• 2010

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수 있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• Structural Engineering and Mechanics
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• 제29권2호
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• pp.223-235
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• 2008

This paper focuses on the application of artificial neural networks (ANN) for optimal design of tensegrity grid as light-weight roof structures. A tensegrity grid, 2 m ${\times}$ 2 m in size, is fabricated by integrating four single tensegrity modules based on half-cuboctahedron configuration, using galvanised iron (GI) pipes as struts and high tensile stranded cables as tensile elements. The structure is subjected to destructive load test during which continuous monitoring of the prestress levels, key deflections and strains in the struts and the cables is carried out. The monitored structure is analyzed using finite element method (FEM) and the numerical model verified and updated with the experimental observations. The paper then explores the possibility of applying ANN based on multilayered feed forward back propagation algorithm for designing the tensegrity grid structure. The network is trained using the data generated from a finite element model of the structure validated through the physical test. After training, the network output is compared with the target and reasonable agreement is found between the two. The results demonstrate the feasibility of applying the ANNs for design of the tensegrity structures.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1915-1920
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• 2004

An edge tone is the discrete tone or narrow-band sound produced by an oscillating free shear layer impinging on a rigid surface. In this paper we present a two-dimensional edge tone to predict the frequency characteristics of the discrete oscillations of a jet-edge feedback cycle by the finite difference lattice Boltzmann method. We use a new lattice BGK compressible fluid model that has an additional term and allow larger time increment comparing a conventional FDLB model, and also use a boundary fitted coordinates. The jet is chosen long enough in order to guarantee the parabolic velocity profile of the jet at the outlet, and the edge consists of a wedge with an angle of ${\alpha}=23^{\circ}$ . At a stand-off distance ${\omega}$ , the edge is inserted along the centreline of the jet, and a sinuous instability wave with real frequency f is assumed to be created in the vicinity of the nozzle and to propagate towards the downstream. We have succeeded in capturing very small pressure fluctuations result from periodically oscillation of jet around the edge. That pressure fluctuations propagate with the sound speed. Its interaction with the wedge produces an irrotational feedback field which, near the nozzle exit, is a periodic transverse flow producing the singularities at the nozzle lips.

• 한국항공우주학회지
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• 제37권6호
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• pp.556-561
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• 2009

다양한 비행환경에서 장시간 체공하며 원격 조종되는 무인항공기에서 추진시스템을 신뢰성 있게 운영하는 것은 매우 중요하다. 스마트 무인기의 수직 이착륙 및 전진 비행에 사용 되는 터보축엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있을 것이다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용 손상된 양을 정확히 진단할 수 있는 방법을 새로이 제안하였다. 제안된 진단방법은 단일손상은 물론 다중손상도 진단할 수 있다.

• 대한조선학회논문집
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• 제60권2호
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• pp.110-119
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• 2023

The prediction of ship resistance performance is typically obtained by Computational Fluid Dynamics (CFD) simulations or model tests in towing tank. However, these methods are both costly and time-consuming, so hull-form designers use statistical methods for a quick feed-back during the early design stage. It is well known that results from statistical methods are often less accurate compared to those from CFD simulations or model tests. To overcome this problem, this study suggests a new approach using a Convolution Neural Network (CNN) with voxelized hull-form data. By converting the original Computer Aided Design (CAD) data into three dimensional voxels, the CNN is able to abstract the hull-form data, focusing only on important features. For the verification, suggested method in this study was compared to a parametric method that uses hull parameters such as length overall and block coefficient as inputs. The results showed that the use of voxelized data significantly improves resistance performance prediction accuracy, compared to the parametric approach.

• 한국터널지하공간학회 논문집
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• 제2권3호
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• pp.25-36
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• 2000

본 연구에서는 합리적이고 공학적인 터널 해석 방법을 제시하기 위해, 시공 중 막장에서 관찰된 신뢰성 높은 암석 및 암반 평점분류 방식과 실내시험을 근거로 하는 일반화된 Hoek-Brown의 현장 암반 모델을 현재 시공이 완료된 지하철 터널 공사 현장의 계측자료와 비교 분석하였다. 그 결과로서 실무적인 측면에서의 터널해석을 위한 일반화된 Hoek-Brown 현장 암반모델의 국내 적용성을 제시하고 적용으로 인한 지반 입력물성치에 대한 타당성을 Trueman과 Trunk의 경험적인 추정식으로 검증하고자 한다. 그러나 불량한 암반의 RMR 값은 정확도가 떨어지기 때문에 일반화된 Hoek-Brown의 현장 암반모델의 적용성에 문제가 있으나, 시공 중 계측자료로 보완함으로서 위험도가 높은 불량암반의 적용성을 평가하였다. 본 연구를 통해서 암석의 경험적인 파괴규준인 일반화된 Hoek-Brown 현장 암반모델을 적용하여 변형과 강도에 과한 암반 입력물성치를 결정하는 과정에서 GSI하한치 = RMR-5를 사용함으로서 현장에서 안정해석의 정확도를 높일 수 있음을 알 수 있다. 단, 여기서는 편마암의 mi=33, 풍화암의 최저치 ${\sigma}ci=100t/m^2$ 이고 GSI는 RMR Chart의 해당연도와 상관없이 동일하다는 조건에서 이루어졌다.

• Asian-Australasian Journal of Animal Sciences
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• 제25권11호
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• pp.1553-1558
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• 2012

This study was conducted to investigate the effects of stocking density or group size on feed intake, daily gain, and carcass characteristics of Hanwoo (Korean indigenous breed) steers reared from 7 months to 31 months of age. Thirty Hanwoo steers were divided into four groups with three replicates each (a total of 12 pens). In each group, one (G1), two (G2), three (G3), and four steers (G4) per pen were allocated as treatments. Pen size was $32.0m^2$, and therefore Hanwoo steers in G1, G2, G3, and G4 were reared under different space allowances, i.e. 32.0, 16.0, 10.6, and $8.0m^2$/steer, respectively. Steers were reared following a conventional beef cattle management method in Korea, and were offered a fixed amount of commercial concentrate with ad libitum forages. Results were subjected to analysis of variance with stocking density as the main effect, and significance was declared at p<0.05. Although total feed intake was not significantly altered, it numerically increased in animals of low stocking density (G1) compared to those subjected to high stocking density treatment (i.e. G4). Feed conversion ratio was higher (p<0.05) in G3 compared to G1 and G2. Animals in G1 (low stocking density) grew faster (p<0.05) than those of high stocking density (G3 and G4). Back fat thickness, meat yield index, and meat yield grade were similar among all levels of stocking density. However, longissimus muscle area was larger in G1 and G2 (p<0.01) compared to G3 and G4, and animals in G3 produced smaller carcasses (p<0.05). Carcass quality traits, including marbling score, meat color, fat color, texture, maturity and meat quality grade, as determined by a group of experts, were not significantly different among the treatments. In conclusion, lower stocking density resulted in increased feed efficiency, daily gain, and carcass weight in Hanwoo steers. However it remains unclear whether such differences are the results of stocking density or group size, or a combination of both. Nonetheless, these results confirm previous studies reporting a negative effect of increased stocking density on animal productivity. Further, animal welfare under an intensive farming system in relation to economical return is discussed.

• 농업과학연구
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• 제48권3호
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• pp.387-396
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• 2021

This study was conducted to provide basic data for efficient Hanwoo beef production by conducting a comparison of growth performance, blood metabolites, testosterone, and carcass characteristics of Hanwoo according to complete and hemi-castration. Twelve Hanwoo calves were allotted to two treatment groups as follows: CC = complete-castration and HC = hemi-castration method of removing only one testicle. At the end of the test, the body weight was 66 kg higher in HC than in CC, and the average daily gain increased by 12.6% (p < 0.05). The feed conversion ratio was significantly improved in HC compared to CC (p < 0.05). Serum cholesterol and triglyceride concentrations were significantly higher in CC than HC in both the growing and fattening periods (p < 0.05), and serum testosterone concentrations before castration were similar between HC and CC, but steadily increasing in HC after castration. Back fat thickness and marbling score were significantly higher in CC than HC in the entire period (p < 0.01). In the results of this study, hemi-castration can improve body weight gain and feed conversion ratio due to the influence of male hormones compared to complete castration, but it is considered that there will be difficulties in producing high-quality meat with a high marbling score.

• 산업경영시스템학회지
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• 제38권4호
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• pp.177-183
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• 2015

Incremental sheet metal forming is a manufacturing process to produce thin parts using sheet metals by a series of small incremental deformation. The process rarely needs dedicated dies and molds, thus, preparation time for the process is relatively short as to be compared to conventional metal forming. Spring back in sheet metal working is very common, which causes critical errors in dimensions. Incremental sheet metal forming is not fully investigated yet. Hence, incremental sheet metal forming frequently produces inaccurate parts. This paper proposes a method to minimize dimensional errors to improve shape accuracy of products manufactured by incremental forming. This study conducts experiments using an exclusive incremental forming machine and the material for these experiments are sheets of aluminum AL1015. This research defines a process parameter and selects a few factors for the experiments. The parameters employed in this paper are tool feed rate, tool diameter, step depth, material thickness, forming method, dies applied, and tool path method. In addition, their levels for each factor are determined. The plan of the experiments is designed using orthogonal array $L_8$ ($2^7$) which requires minimum number of experiments. Based on the measurements, dimensional errors are collected both on the tool contacted surfaces and on the non-contacted surfaces. The distances between the formed surfaces and the CAD models are scanned and recorded using a commercial software product. These collected data are statistically analyzed and ANOVAs (analysis of variances) are drawn up. From the ANOVAs, this paper concludes that the process parameters of tool diameter, forming depth, and forming method are the significant factors to reduce the errors on the tool contacted surface. On the other hand, the experimental factors of forming method and dies applied are the significant factors on the non-contacted surface. However, the negative forming method always produces better accuracy than the positive forming method.

• Computers and Concrete
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• 제12권6호
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.