Papers with Code 아카이브

Structural Health Monitoring

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Jack-Poole/Statistic-Alignment

공식 코드

On statistic alignment for domain adaptation in structural health monitoring

https://github.com/caglarmert/mage

공식 코드

Exploring Challenges in Deep Learning of Single-Station Ground Motion Records

https://github.com/eml-eda/tle-supervised

공식 코드

Foundation Models for Structural Health Monitoring

https://github.com/giarcieri/hidden-markov-models

공식 코드

Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems

https://github.com/giuliosiracusano/deeplearning_crack_classification

공식 코드

Automatic crack classification by exploiting statistical event descriptors for Deep Learning

https://github.com/gledsonrt/pigpdynamicmodalloadreconstruction

공식 코드

Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions

https://github.com/gledsonrt/pigptimoshenkobeam

공식 코드

Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes

https://github.com/hesam-92-19/transfer_learning_shm

공식 코드

Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping

https://github.com/human-analysis/neural-structural-identification

공식 코드

NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems

https://github.com/liouvill/neuralekf

공식 코드

Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems

https://github.com/mahindrautela/anovae-delamshm

공식 코드

Deep generative models for unsupervised delamination detection using guided waves

https://github.com/marcusha94/structural-dynamics-pinns

공식 코드

Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks

https://github.com/matthewszhang/fastcan

공식 코드

Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring

https://github.com/miirho3ein/real_time_bhm

공식 코드

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

https://github.com/rohdelab/PyTransKit

공식 코드

Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform

https://github.com/shrutisawant099/damage-classification-using-feature-engineering

공식 코드

Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health Monitoring

https://github.com/smart-objects/impact-events-dataset

공식 코드

Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin Plate

https://github.com/smbrealy/hierarchical_foundations

공식 코드

Multitask learning for improved scour detection: A dynamic wave tank study

https://github.com/zhaoyj21/fcg

공식 코드

Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

https://github.com/MatthewSZhang/ffs

Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.