Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classification performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with five common image classification datasets and the Bert model with four common text classification datasets. Compared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.Comment: 9 pages,6 figure

Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training

Traditional Encrypted Traffic Classification (ETC) methods face a significant challenge in classifying large volumes of encrypted traffic in the open-world assumption, i.e., simultaneously classifying the known applications and detecting unknown applications. We propose a novel Open-World Contrastive Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training to obtain a robust feature representation. Based on this, we determine the spherical mapping space to find the marginal flows for each known class, which are used to train GANs to synthesize new flows similar to the known parts but do not belong to any class. These synthetic flows are assigned to Softmax's unknown node to modify the classifier, effectively enhancing sensitivity towards known flows and significantly suppressing unknown ones. Extensive experiments on three datasets show that OWCP significantly outperforms existing ETC and generic open-world classification methods. Furthermore, we conduct comprehensive ablation studies and sensitivity analyses to validate each integral component of OWCP.Comment: Accepted by 2023 IEEE ISCC, 6 pages, 5 figure

Data Informed Model Test Design With Machine Learning – An Example in Nonlinear Wave Load on a Vertical Cylinder

Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering—nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods, including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several “interpretable” decisions which can be explained with physical insights

An investigation of high-order harmonics in the pressure field around a vertical cylinder in steep wave conditions

Offshore structures, encompassing foundations for offshore wind turbines, supports for marine renewable energy devices, bridge piers, and floating vessels, are consistently subjected to severe environmental loads. These loads often dictate the design criteria. Understanding the physics and statistics of wave-structure interaction, especially under non-linear loads experienced in extreme conditions, remains a complex and partially unresolved challenge. Notably, secondary load cycles significantly contribute to the ’ringing’ responses in cylindrical structures, as discussed in previous studies (e.g., Grue et al. (1993), Chaplin et al. (1997)). This paper focuses on analysing loads in focused wave groups, representing short-term extreme wave conditions, on bottom-mounted vertical cylinders relevant to fixed offshore wind turbines. Pressure contour plots over the cylinder’s surface were previously examined by Ghadirian & Bredmose (2020) while studying secondary load cycles. In this research, we adopt the phase-based harmonic separation method for wave forces (Fitzgerald et al. (2014)) to analyse the pressure contour plots. This method effectively isolates harmonic pressure components from the total pressures, enabling a novel exploration of the mechanisms behind secondary load cycles from the perspective of high-order harmonics on the cylinder surface

A new Gaussian Process based model for non-linear wave loading on vertical cylinders

We aim to establish a fast and accurate model for fast prediction of nonlinear loading on vertical cylinders such as are typically used for fixed offshore wind turbines. We follow a ‘Stokes-type’ force model and approximate the amplitude of the higher harmonics of force by relating these to the linear force time series raised to appropriate power through amplitude and phase coefficients. We reanalyse previous experimental data and perform new experiments to expand the parameter space and establish a force coefficients database for engineering applications. A machine learning model is used to interpolate the database and make predictions on the higher order force coefficients. The machine learning model also provides a cross-validated confidence interval to indicate the prediction uncertainty and reflect model reliability. We further extend the prediction capability to unidirectional random waves with a novel force segmentation method, which localised wave groups from the random background. The new Stokes-Gaussian Process (Stokes-GP) model developed can provide engineering predictions of nonlinear wave loading on a cylinder for individual wave groups and random seas, which are straightforward to apply and fast to compute and the important higher-order loading components are considered. This will significantly improve the accuracy of the loading prediction and the ease of application for force predictions.</p

Recent experimental wave load study on bottom fixed vertical cylinder study at the Kelvin Hydrodynamics Laboratory

Monopile wind turbines are typically anchored to the seabed using a large steel tube, when subjected to extreme wave loading, the monopile foundation can experience high stresses and strains that can lead to fatigue and failure, particularly when the higher-order components of the wave loading match the structural natural frequency. Therefore, a better understanding of extreme wave loading on a monopile structure is critical for engineers. The Kelvin Hydrodynamics Laboratory of the University of Strathclyde has been involved in extreme wave loading on bottom fixed cylinders since 2018, including utilising methods like four-phase decomposition and conducting research on various factors. Our recent experimental studies at the Kelvin Hydrodynamics Laboratory have provided valuable insights into wave loading on bottom fixed vertical cylinders. By utilizing the four phase separation method, we were able to extract higher order components in focus wave loading, allowing for a more comprehensive analysis. Additionally, the generation of 100 k waves allowed us to delve deeper into understanding the response of the cylinders to different wave loadings More recent experiments focus on secondary load cycle, breaking wave impact and wider design loading space. These findings contribute to the existing knowledge of wave cylinder interactions and provide a foundation for further research in this field

Dara informed model test design with machine learning - An example in nonlinear load on vertical cylinder

Model tests are common for coastal and offshore engineering purposes. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The optimal design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering – nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The field data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several 'interpretable' decisions which can be explained with physical intuition

Information Propagation Prediction Based on Key Users Authentication in Microblogging

In microblogging, key users are a significant factor for information propagation. Key users can affect information propagation size while retweeting the information. In this paper, to predict information propagation, we propose a novel linear model based on key users authentication. This model mines key users to dynamically improve the linear model while predicting information propagation. So our model can not only predict information propagation but also mine key users. Experimental results show that our model can achieve remarkable efficiency on predicting information propagation problem in real microblogging networks. At the same time, our model can find the key users who affect information propagation

An investigation of high-order harmonics in the pressure field around a vertical cylinder in steep wave conditions

Offshore structures, encompassing foundations for offshore wind turbines, supports for marine renewable energy devices, bridge piers, and floating vessels, are consistently subjected to severe environmental loads. These loads often dictate the design criteria. Understanding the physics and statistics of wave-structure interaction, especially under non-linear loads experienced in extreme conditions, remains a complex and partially unresolved challenge. Notably, secondary load cycles significantly contribute to the ’ringing’ responses in cylindrical structures, as discussed in previous studies (e.g., Grue et al. (1993), Chaplin et al. (1997)). This paper focuses on analysing loads in focused wave groups, representing short-term extreme wave conditions, on bottom-mounted vertical cylinders relevant to fixed offshore wind turbines. Pressure contour plots over the cylinder’s surface were previously examined by Ghadirian &amp; Bredmose (2020) while studying secondary load cycles. In this research, we adopt the phase-based harmonic separation method for wave forces (Fitzgerald et al. (2014)) to analyse the pressure contour plots. This method effectively isolates harmonic pressure components from the total pressures, enabling a novel exploration of the mechanisms behind secondary load cycles from the perspective of high-order harmonics on the cylinder surface

Data Informed Model Test Design With Machine Learning – An Example in Nonlinear Wave Load on a Vertical Cylinder

Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering—nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods, including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several “interpretable” decisions which can be explained with physical insights