Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Engineering
Cybert: Cybersecurity Claim Classification By Fine-Tuning The Bert Language Model, Kimia Ameri, Michael Hempel, Hamid Sharif, Juan Lopez Jr., Kalyan Perumalla
Cybert: Cybersecurity Claim Classification By Fine-Tuning The Bert Language Model, Kimia Ameri, Michael Hempel, Hamid Sharif, Juan Lopez Jr., Kalyan Perumalla
Department of Electrical and Computer Engineering: Faculty Publications
We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting …
A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
Department of Electrical and Computer Engineering: Faculty Publications
This paper presents a genetic algorithm (GA)- support vector machine (SVM) hybrid classifier for multiclass fault identification of drivetrain gearboxes in variable-speed operational conditions. An adaptive feature extraction algorithm is employed to effectively extract the features of gearbox faults from the stator current signal of an AC machine connected to the gearbox. The multiclass GA-SVM classifier is used to identify the faults in the gearbox according to the fault features extracted. A GA is designed to find the optimal parameters of the SVM to obtain the best classification accuracy. The proposed hybrid classifier is validated on a gearbox connected with …
Low-Power Analog Processing For Sensing Applications: Low-Frequency Harmonic Signal Classification, Daniel J. White, Peter E. Williams, Michael W. Hoffman, Sina Balkir
Low-Power Analog Processing For Sensing Applications: Low-Frequency Harmonic Signal Classification, Daniel J. White, Peter E. Williams, Michael W. Hoffman, Sina Balkir
Department of Electrical and Computer Engineering: Faculty Publications
A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fourier Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to …
Adaptive Feature Extraction And Svm Classification For Real-Time Fault Diagnosis Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
Adaptive Feature Extraction And Svm Classification For Real-Time Fault Diagnosis Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
Department of Electrical and Computer Engineering: Faculty Publications
Drivetrain gearboxes play an important role in many modern industrial applications. This paper presents a novel method consisting of adaptive feature extraction and support vector machine (SVM)-based classification for condition monitoring and fault diagnosis of drivetrain gearboxes operating in variable-speed conditions. An adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm are integrated in the proposed method for effective extraction of the features of gearbox faults from the stator current signal of the AC electric machine connected to the gearbox. A radial basis function kernel-SVM classifier is designed to identify the fault in the gearbox according to …