• Title/Summary/Keyword: 모델합성

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Explicit Nonlinear Finite Element Analysis for Flexure Behavior of FRP-Concrete Composite Beam (FRP-콘크리트 합성보의 휨거동에 관한 외연적 비선형 유한요소해석 연구)

  • Yoo, Seung Woon;Kang, Ga Ram
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.37 no.2
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    • pp.269-276
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    • 2017
  • In order to study ultimate flexure behavior of FRP-concrete composite structures which can replace reinforced concrete structures, ABAQUS, a general purpose analysis program, was utilized for numerical nonlinear analysis of structural performance and behavior characteristics of FRP-concrete composite beams. Explicit nonlinear finite element analysis was conducted and the numerical results were compared with previous experiments. Concrete damaged plasticity model was adopted as material properties of concrete and Euro code was used as compressive stress state. Nonlinear analysis was performed for four different types of FRP-concrete composite beams, and ultimate load and cracking pattern was compared and analyzed. The model suggested in this research was able to simulate ultimate load and cracking pattern properly, it is expected to be utilized in study of precise structural and behavioral characteristics of various FRP-concrete composite structures.

Entity Embeddings for Enhancing Feasible and Diverse Population Synthesis in a Deep Generative Models (심층 생성모델 기반 합성인구 생성 성능 향상을 위한 개체 임베딩 분석연구)

  • Donghyun Kwon;Taeho Oh;Seungmo Yoo;Heechan Kang
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.6
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    • pp.17-31
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    • 2023
  • An activity-based model requires detailed population information to model individual travel behavior in a disaggregated manner. The recent innovative approach developed deep generative models with novel regularization terms that improves fidelity and diversity for population synthesis. Since the method relies on measuring the distance between distribution boundaries of the sample data and the generated sample, it is crucial to obtain well-defined continuous representation from the discretized dataset. Therefore, we propose an improved entity embedding models to enhance the performance of the regularization terms, which indirectly supports the synthesis in terms of feasible and diverse populations. Our results show a 28.87% improvement in the F1 score compared to the baseline method.

Compositional Safety Analysis for Embedded Systems using the FSM Behavioral Equivalence Algorithm (FSM의 행위 일치 알고리즘을 이용한 임베디드 시스템의 합성적 안전성 분석 기법)

  • Lee, Woo-Jin
    • The KIPS Transactions:PartD
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    • v.14D no.6
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    • pp.633-640
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    • 2007
  • As the embedded systems closely related with our living become complex by interoperating each other via internet, the safety issue of embedded systems begins to appear For checking safety properties of the system interactions, it is necessary to describe the system behaviors in formal methods and provide a systematic safety analysis technique. In this research, the behaviors of an embedded system are described by Labeled Transition Systems(LTS) and its safety properties are checked on the system model. For enhancing the existing compositional safety analysis technique, we perform the safety analysis techniques by checking the behavioral equivalence of the reduced model and a property model after reducing the system model in the viewpoint of the property.

Behavior of Negative Moment Region of Continuous Double Composite Railway Bridges (이중합성 2거더 연속 철도교의 부모멘트부 거동)

  • Shim, Chang Su;Kim, Hyun Ho;Yun, Kwang Jung
    • Journal of Korean Society of Steel Construction
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    • v.18 no.3
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    • pp.339-347
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    • 2006
  • This study proposes a double-composite section to enhance the s serviceability of twin-girder railway bridges, especially in terms of the flexural stiffness of the composite section in negative-moment regions. This paper deals with experiments on continuous twin-girder bridge models with 5m-5m span length with the proposed double-composite action. From results of static tests on the bridge models, several design considerations were investigated including effective width, shear connection and ultimate strength of the double-composite concrete slab showed full shear connection, which verified the suggested empirical equation. From the flexural behavior of the double-composite section, the effective width of the bottom concrete slab can be evaluated as that of the concrete slab under compression. The ultimate flexural strength of the bridge models verified the validity of the rigid plastic analysis of the double-composite section. Design guidelines were suggested based on the test results.

Fast Harmonic Synthesis Method for Sinusoidal Speech-Audio Model (정현파 음성-오디오 모델의 빠른 하모닉 합성 방법)

  • Kim, Gyu-Jin;Kim, Jong-Hark;Jung, Gyu-Hyeok;Lee, In-Sung
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.4 s.316
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    • pp.109-116
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    • 2007
  • Most harmonic synthesis methods using phase information employ a quadratic or cubic phase interpolation. The methods are computationally expensive to implement because every component sinewave must be synthesized on a per sample basis. In this paper, we propose a fast harmonic synthesis method for sinusoidal speech/audio coding based on the quadratic and cubic phase function to overcome the complexity problem. To derive the fast harmonic synthesis method, we define the over-sampling function and phase modulation function by constraining the parameter of phase function to be independent for harmonic index and derive the fast synthesis method using IFFT. Experimental results show that the proposed method significantly reduce the complexity of conventional cosine synthesis method while maintaining the performance.

Mixing Length Model of Combined Flow Bed Friction (합성류 전단력 계산을 위한 혼합거리 모델)

  • 유동훈
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.1 no.1
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    • pp.8-14
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    • 1989
  • A mathematical model for computing the bed friction of combined wave-current flow has been developed based on the Prandtl's mixing length theory. Using various approximate expressions, solutions are obtained explicitly. The computational results are compared and found in reasonable agreements with the data of field measurements.

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Development of Image Defect Detection Model Using Machine Learning (기계 학습을 활용한 이미지 결함 검출 모델 개발)

  • Lee, Nam-Yeong;Cho, Hyug-Hyun;Ceong, Hyi-Thaek
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.3
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    • pp.513-520
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    • 2020
  • Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.

Artificial neural network for classifying with epilepsy MEG data (뇌전증 환자의 MEG 데이터에 대한 분류를 위한 인공신경망 적용 연구)

  • Yujin Han;Junsik Kim;Jaehee Kim
    • The Korean Journal of Applied Statistics
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    • v.37 no.2
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    • pp.139-155
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    • 2024
  • This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

Spectrum Based Excitation Extraction for HMM Based Speech Synthesis System (스펙트럼 기반 여기신호 추출을 통한 HMM기반 음성합성기의 음질 개선 방법)

  • Lee, Bong-Jin;Kim, Seong-Woo;Baek, Soon-Ho;Kim, Jong-Jin;Kang, Hong-Goo
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.1
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    • pp.82-90
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    • 2010
  • This paper proposes an efficient method to enhance the quality of synthesized speech in HMM based speech synthesis system. The proposed method trains spectral parameters and excitation signals using Gaussian mixture model, and estimates appropriate excitation signals from spectral parameters during the synthesis stage. Both WB-PESQ and MUSHRA results show that the proposed method provides better speech quality than conventional HMM based speech synthesis system.

Distortion of Spectrum Envelope with Change of Pitch Period in the Cepstrum Analysis-synthesis System (켑스트럼 분석합성형 음성합성 시스템에서의 피치변경에 따른 스펙트럼 포락 왜곡 현상에 관한 연구)

  • 김연준
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1992.06a
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    • pp.54-57
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    • 1992
  • 음성합성에 있어서 음의 자연성을 합성시키는 문제는 크게 두가지로 나누어진다. 첫째는 합성음을 원음에 가깝게 구현하려는 합성방법 자체의 문제로, 언어 합성이 가지고 있는 일반적인 문제이다. 또 다른 문제는 운율에 관한 것으로 낱말 또는 문장 내에서의 운율에 따라 합성음의 자연성이 좌우된다. 이러한 운율에 따라 합성음의 자연성이 좌우된다. 이러한 운율의 조절에는 지속시간, 피치, 그리고 음의 세기 등이 이용된다. 켑스트럼을 이용하여 분석합성을 하는 경우, pole-zero 모델로 스펙트럼 포락을 근사하므로 원음에 충실하고, 필터계수와 구동정보를 분리하여 분석, 합성하므로 인위적인 운율의 조절이 용이하여 음성합성이 가지는 위의 두가지 문제를 해결하는데 적합하다고 판단된다. 본 연구에서는 켑스트럼을 이용하여 분석합성 시스템을 구성하였다. 음성 합성 과정에서, 운율 조절 파라미터중의 하나인 피치 주기의 변경에 따라 스펙트럼 포락의 왜곡에 대해 살펴보고, 왜곡을 최소화하는 방안을 제안한다.

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