• Composites Research
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• 제26권4호
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• pp.207-212
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• 2013

본 연구에서는 유리강화섬유폴리머(GFRP)와 탄소강화섬유폴리머(CFRP)로 적층된 조류력 블레이드의 스파 캡(Spar cap)을 대상으로 끝단 처짐의 제한에 따른 단방향(UD) GFRP의 적층 두께를 최적화 하였다. 또한 도출된 적층 두께에 따른 블레이드 내부의 응력의 변화와 블레이드의 재료비용을 확인하였다. 비선형 최적화에 뛰어난 순차 이차방정식 프로그래밍(SQP) 알고리즘을 사용하였고, 목적함수를 계산하기 위하여 상용 유한요소해석 프로그램인 Abaqus/Standard와 연계하였다. UD CFRP의 적층 두께가 9 mm로 제한된 경우, 끝단 처짐이 감소함에 따라 UD GFRP의 적층 두께가 증가하였다. 즉, 최적화된 스파 캡의 무게는 최대 96.2% 증가였으며 최대 인장응력은 최대 24.6% 감소하였다. 끝단 처짐이 126.83 mm로 제한된 경우, UD CFRP의 적층 두께가 줄어듦에 따라 UD GFRP의 적층 두께가 증가하였다. 이로 인하여 무게는 최대 40.1% 증가하였지만 재료비용은 최대 16.97% 감소하였다. 본 연구에서 제시한 블레이드 스파 캡의 최적화된 두께를 바탕으로 조류력 블레이드의 무게, 내부의 최대 응력과 재료비용의 상관관계를 제시하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.128-133
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• 2002

The main obstacle to high track density in HDD is the structural resonances of the suspension. The most critical mode is sway mode and second torsion mode, when a data is read and written. It is common fact that the effect of two modes is smaller when a thickness is bulky. But the stiffness of suspension is smaller, the slider can follow a disk better. Because these two fact are reciprocal, a compromise is needed. So we investigated another method to improve band width without changing of the thickness of suspension but with changing of the shape. In this paper, we use two method - Sensitivity analysis and SIP using ADS. And we obtained the optimized value close to target value.

• Nuclear Engineering and Technology
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• 제53권11호
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• pp.3764-3771
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• 2021

Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.

• 화약ㆍ발파
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• 제31권2호
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• pp.14-24
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• 2013

도심지에서 암반 제거 작업에 시공되어 온 방법 중 가장 효과적인 방법은 소량의 화약류를 사용하여 장약 전색한 후 발파하여 암반에 균열을 발생시켜 암석을 제거하는 방법인데, 환경적인 요인으로 인하여 그 사용에 제한을 받는 경우가 많아지고 발파 불가 지역이 늘어나고 있는 실정이다. 이 공법은 암반에 천공된 공속에 장약을 할 때 전색보호판을 이용하고 같은 시차의 뇌관과 화약으로 다단 장약/전색하고 다단발파기를 이용하여 최적화 된 정밀제어를 함으로써 진동을 감소시켜 발파하는 다단식분산발파 방법으로 모든 현장에 사용 가능하지만 특히 진동과 관련된 분쟁이 큰 도심 지역의 암반 제거에 있어서 더 효과적으로 사용할 수 있다. 이 공법은 일반적으로 천공되어지는 짧은 천공장(1.2~3.0미터)으로 인해 다단 장약 후 지연 기폭이 힘든 구간에서도 뇌관선이 단락 되거나 폭발화약 주변의 화약이 사압을 받는 일이 없이 쉽게 발파패턴을 설계하여 사용 할 수 있다.

• 웰빙융합연구
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• 제6권2호
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• 해양환경안전학회지
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• 제24권4호
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• pp.467-474
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• 2018

본 연구의 목적은 2가지 선속에서 운항하는 선박의 선형 설계 자동화에 관한 것이다. 가장 기본적인 선박의 형상을 가지는 60계열($C_B=0.6$) 선박을 대상선박으로 선택하여 연구를 수행하였다. 선박 형상의 향상 방향은 저항성능 향상의 관점이며, 특히 선박의 형상과 밀접한 관계를 가지는 조파저항성능을 향상하기 위한 선박 형상 설계 자동화를 수행하였다. 본 연구의 목적을 실현하기 위하여 최적화 기법과 저항 성능을 예측하는 기법 그리고 선형의 형상을 변경하는 기법을 접목하여 선박 형상 설계 자동화 소프트웨어를 개발하였으며, 개발된 소프트웨어를 대상선박에 적용하였다. 최적화 기법으로는 순차이차계획법(sequential quadratic programming method)를 사용하였으며, 조파저항성능을 예측하기 위하여 포텐셜기저 패널법(potential-based panel method)을 사용하였다. 선박 형상의 변경은 가우시안형 수정함수법(Gaussian-type modification function method)를 개발하여 적용하였다. 개발된 소프트웨어를 사용하여 대상선박의 서로 다른 두 가지 선속에 대하여 설계를 수행하고 그 결과를 서로 비교하였다. 그리고 개발된 프로그램의 타당성을 검증하기 위하여 모형시험을 수행하여 구한 실험값과 수치해석을 수행하여 구한 계산값을 서로 비교하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.139-142
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• 1991

The design variables of SLIM used for 3-D conveyor system are optimized by nonlinear programing. Five design variables are selected as independent ones and object function is expressed as a combination of the weight and the normal force of the motor. Maximum flux density in the teeth, primary length and starting thrust are chosen as constraint functions. Goodness factor considering of conveying characteristic is also included in the constraints. In this paper sequential unconstrained minimization technique(SUMT) and variable metric method are used to solve the nonlinear problem.

• The Journal of the Acoustical Society of Korea
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• 제23권3E호
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• 한국정밀공학회지
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• 제33권7호
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• pp.559-564
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• 2016

The objectives of this paper are to develop a finite element analysis model to analyze press-fitted and bending load conditions in a press-fitted assembly, and propose a hub shape optimization method to minimize contact pressure near the shaft contact edge. Numerical asymmetric-axisymmetric finite element models have been developed to predict contact stress on press-fitted shafts. The global optimization method, genetic algorithm, local optimization method, and sequential quadratic programming were applied to a press-fitted assembly to optimize the hub contact edge geometry. The results showed that the maximum contact pressure with the optimized hub shape decreased more than 60 % compared to conventional hubs and the maximum contact stress affecting fatigue life was reduced about 47 %. Hub shape optimization can be useful to increase the load capability of press fits in terms of wear and fatigue behavior.