High-Dimensional Reliability Analysis with Error-Guided Active-Learning Probabilistic Support Vector Machine: Application to Wind-Reliability Analysis of Transmission Towers

Song, Chaolin; Shafieezadeh, Abdollah; Xiao, Rucheng

doi:10.1061/(asce)st.1943-541x.0003332

Cited by 16 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the extreme gradient boosting (XGBoost) model is used for the classification task. Compared to the other machine learning models commonly applied in the field of reliability analysis such as logistic regression, 41,42 and support vector machines, 43 XGBoost provides many benefits. These advantages include the handling of high dimensional data, supporting regularization to avoid overfitting, and the ability to perform cross‐validation at each iteration to constantly monitor the performance of the model 44 .…”

Section: Methodsmentioning

confidence: 99%

Multi‐hazard typhoon and earthquake collapse fragility models for transmission towers: An active learning reliability approach using gradient boosting classifiers

Jeddi

Shafieezadeh

Hur

et al. 2022

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

Natural hazards such as earthquakes and typhoons pose a considerable threat to the power transmission grid. In regions prone to both hazards, the overhead transmission infrastructure may experience multi-hazard effects where two consecutive hazards can occur within an interval shorter than the time that would be needed to repair the system for damages occurred in the first event. This consequently increases the susceptibility to damage during the subsequent hazard.

show abstract

Section: Methodsmentioning

confidence: 99%

Multi‐hazard typhoon and earthquake collapse fragility models for transmission towers: An active learning reliability approach using gradient boosting classifiers

Jeddi

Shafieezadeh

Hur

et al. 2022

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the feasibility stage, new sample points can be selected by maximizing the feasibility learning function as shown in Equation ( 16) 32 :…”

Section: Feasibility Learning Function Maximizationmentioning

confidence: 99%

“…In the feasibility stage, new sample points can be selected by maximizing the feasibility learning function as shown in Equation () 32 :

Max L (x) = P_{wrong class} (x) * \overset{true¯}{d} (x)

where

P_{wrong class} (x)

is the probability of wrong classification,

\overset{true¯}{d} (x)

is the normalized distance to nearest neighbor.

P_{wrong class} (x)

is defined as:

P_{wrong class} (x) = \min (P (- 1| x), P (+ 1| x))

where

P (- 1| x)

and

P (+ 1| x)

are calculated based on the SVM prediction

\overset{truê}{ψ} (x)

, as shown below:

P (- 1| x) = \frac{1}{2 + \overset{truê}{f} (x)}, if \overset{truê}{ψ} (x) > 0

P (- 1| x) = \frac{1}{1 + \exp (\overset{truê}{f} ())}

…”

Section: Proposed Dynamic Switching Sbo Frameworkmentioning

confidence: 99%

A surrogate‐based framework for feasibility‐driven optimization of expensive simulations

Tian,

Ierapetritou

2024

AIChE Journal

View full text Add to dashboard Cite

Surrogate‐based optimization approaches have been widely adopted in industrial problems due to their potential to reduce the number of simulation runs required in the optimization process. The surrogate‐based optimization framework has been extended to feasibility analysis in pharmaceutical manufacturing to characterize the design space. Most surrogate‐based approaches for feasibility analysis are limited to the construction of a regression model for the feasibility function. In this work, we developed a framework with the feasibility problem considered as a classification problem, and additional stages introduced to improve local exploitation and global exploration. We illustrate the efficiency of the proposed framework on three test problems and implement it in a realistic case study describing the production of solid‐based drugs using wet granulation, aimed to reduce the operation cost, improve product quality, and increase process flexibility and robustness.

show abstract

“…The instance that is closest to the hyperplane is called the support vector. According to Song, the advantages of the SVM algorithm are that it provides the smallest generalization error of other methods and can solve problems with high dimensions and limited samples [24]. The weakness of SVM is its complicated computation for high-dimensional data [25].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Mining the public sentiment for wayang climen preservation and promotion

Wibawa,

Rosyidin,

Kurniawati

et al. 2023

Int. J. Vis. Perform. Arts

View full text Add to dashboard Cite

Indonesia is a country that has a variety of cultural arts, one of which is shadow puppetry (Wayang). Wayang, in a staged, simple, and minimalist manner, is called Wayang Climen. Wayang Climen has been performed since the COVID-19 pandemic as a solution to keep working while still complying with health protocols. Utilization through YouTube social media attracts people to watch and provide opinions through comments. This opinion is beneficial and can be used as a feasibility study through sentiment analysis information classified as positive, negative, and neutral opinions. Sentiment analysis determines a person's opinion and tendency to opinionated sentences. The methods used are Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The dataset comes from YouTube comments of Dalang Seno and Ki Seno Nugroho. The best accuracy is generated by SVM (70.29%). The positive sentiment shows the public's appreciation for the Wayang Climen performance, which ultimately represents the performance even though it is staged densely. This research contributes to effectively utilizing digital platforms for cultural preservation and audience engagement during challenging times, demonstrating the potential for innovative solutions in traditional arts and entertainment.

show abstract

High-Dimensional Reliability Analysis with Error-Guided Active-Learning Probabilistic Support Vector Machine: Application to Wind-Reliability Analysis of Transmission Towers

Cited by 16 publications

References 45 publications

Multi‐hazard typhoon and earthquake collapse fragility models for transmission towers: An active learning reliability approach using gradient boosting classifiers

Multi‐hazard typhoon and earthquake collapse fragility models for transmission towers: An active learning reliability approach using gradient boosting classifiers

A surrogate‐based framework for feasibility‐driven optimization of expensive simulations

Mining the public sentiment for wayang climen preservation and promotion

Contact Info

Product

Resources

About