• Structural Engineering and Mechanics
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• 제44권3호
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• 한국CDE학회논문집
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• 제13권2호
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• pp.131-138
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• 2008

With the advancements of the Internet and CAD data translation techniques, more CAD models are transferred from a CAD system to another through the network and interoperability is getting a common word in the CAD industry. However, when a CAD model is translated for an incompatible system into a neutral format such as STEP or IGES, its precious feature information is lost. When this feature information is lost, the advantage of feature based modeling is not valid any longer, and modification for the model is purely dependent on geometric and topological manipulations. However, the capabilities of the existing methods to modify these feature-independent models are limited as the modification involves a topological change in the model. To address this issue, we present a volumetric method to modify the solid models in neutral format. First, this method selectively decomposes the solid model to separate the portion of interest called feature volume. Next, the designer modifies the feature volume without concerning a topological change. Finally, the feature volume is united with the original solid model to complete the modification process. The results of test cases are presented to attest the usefulness of the proposed method.

• Steel and Composite Structures
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• 제49권6호
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• pp.633-643
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• 2023

In this study, the natural frequencies of Functional Graded Materials (FGM) plates are predicted using Artificial Neural Network (ANN). A model based on Third-order Shear Deformation Theory (TSDT) and FEM is used to train the ANN model. Different training methods are tested to simulate input and output dependency. As this is a parametric model, several architectures and optimization algorithms were tested. The proposed model allows us to minimize the CPU time to evaluate candidate material properties for FGM plate material selection and demonstrate their influence on dynamic behavior. Consequently, the time required for the FGM design process (candidate materials for material selection) and the geometric optimization of the FGM structure would remain reasonable. The ANN model can help industries to produce FGM plates with good mechanical properties of the selected materials. I addition, this model can be used to directly predict vibration behavior by testing a large number of FGM plates, representing all possible combinations of metals and ceramics in today's industry, without having to solve any eigenvalue problems.

• Earthquakes and Structures
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• 제10권4호
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• pp.757-768
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• 2016

In the past decades, effect of near field earthquake on the historical monuments has attracted the attention of researchers. So, many analyses in this regard have been presented. Tunnels as vital arteries play an important role in management after the earthquake crisis. However, digging tunnels and seismic effects of earthquake on the historical monuments have always been a challenge between engineers and historical supporters. So, in a case study, effect of near field earthquake on the historical monument was investigated. For this research, Finite Element Analysis (FEM) in soil environment and soil-structure interaction was used. In Plaxis 2D software, different accelerograms of near field earthquake were applied to the geometric definition. Analysis validations were performed based on the previous numerical studies. Creating a nonlinear relationship with space parameter, time, angular and numerical model outputs was of practical and critical importance. Hence, artificial Neural Network (ANN) was used and two linear layers and Tansig function were considered. Accuracy of the results was approved by the appropriate statistical test. Results of the study showed that buildings near and far from the tunnel had a special seismic behavior. Scattering of seismic waves on the underground tunnels on the adjacent buildings was influenced by their distance from the tunnel. Finally, a static test expressed optimal convergence of neural network and Plaxis.

• 대한조선학회논문집
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• 제59권6호
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• pp.393-399
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• 2022

During the ship hull design process, resistance performance estimation is generally calculated by simulation using computational fluid dynamics. Since such hull resistance performance simulation requires a lot of time and computation resources, the time taken for simulation is reduced by CPU clusters having more than tens of cores in order to complete the hull design within the required deadline of the ship owner. In this paper, we propose a method for estimating resistance performance of ship hull by simulation using a graph neural network. This method converts the 3D geometric information of the hull mesh and the physical quantity of the surface into a mathematical graph, and is implemented as a deep learning model that predicts the future simulation state from the input state. The method proposed in the resistance performance experiment of simple hull showed an average error of about 3.5 % throughout the simulation.

• Smart Structures and Systems
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• 제32권5호
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• pp.297-308
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• 2023

Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• 대한지하수환경학회지
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• 제5권2호
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• pp.80-87
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• 1998

지하수의 유동로가 시설의 성능에 미치는 영향이 큰 방사성폐기물처분시설에서는 암반블럭규모의 흐름은 단열망개념으로 해석하고 있다. 본 연구는 연구지역의 지하수유동 해석을 위하여 3차원 투수성단열망모델을 구축하기 위한 것으로서, 단열의 기하학적 인자 및 수리인자에 대한 확률분포함수를 도출하고, 3차원 단열망모델링과 수리시험 결과를 이용한 모델 교정까지의 과정을 포함한다. 구간별 정압주입시험의 결과를 Cubic law로서 해석한 결과 단열투수량계수는 lognormal분포일 때 6.12$\times$$10^{－7}$ $m^2$/sec이다. 부정류해석에 의한 유동차원은 주로 방사상 내지 구상유동 특성을 보인다. FracMan 코드를 이용해서 추정된 투수성단열밀도는 1.73이고, 이때 암반블럭규모(l00 m$\times$100 m$\times$100 m)로 모사된 투수성단열의 수는 3,080개이다.다.

• 해양환경안전학회지
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• 제28권7호
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• pp.1274-1280
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• 2022

전산유체역학을 사용하는 일반적인 선박의 저항성능 평가는 많은 시간과 비용이 필요하며, 이를 줄이기 위한 다양한 방법이 연구되고 있다. 선박의 주요 치수나 단면을 이용하는 기존의 방법들은 선형에 크게 좌우되는 저항성능을 추정하는데 한계가 있다. 본 논문에서는 선형 격자의 기하학적 정보를 입력으로 선체 표면의 저항성능을 빠르게 추정할 수 있는 심층신경망 모델을 제안한다. Perceiver IO 기반의 제안하는 심층신경망 모델은 시간 단계별로 계산이 필요한 전산유체역학 기법과 달리 바로 저항성능 추정이 가능하며, 저속비대선의 일종인 50K 탱커 선박을 대상으로 한 데이터집합에서 평균 1% 미만의 오차로 저항성능을 추정하는 결과를 보인다.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.155-161
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• 2024

본 논문에서는 기존의 CNN 기반 얼굴 감정 분석 모델에 랜드마크 정보를 특징 벡터로 추가하여 새로운 모델을 제안한다. CNN 기반 모델을 이용한 얼굴 감정 분류 연구는 다양한 방법으로 연구되고 있으나 인식률이 매우 저조한 편이다. 본 논문에서는 CNN 기반 모델의 성능을 향상시키기 위하여 CNN 모델에 ASM으로 구한 랜드마크 기반 완전 연결 네트워크를 결합함으로써 얼굴 표정 분류 정확도를 향상시키는 알고리즘을 제안한다. CNN 모델에 랜드마크를 포함시킴으로써 인식률이 VGG 0.9%, Inception 0.7% 개선되었으며, 랜드마크에 FACS 기반 액션 유닛 추가를 통하여 보다 VGG 0.5%, Inception 0.1%만큼 향상된 결과를 얻을 수 있음을 실험으로 확인하였다.