Search Results for author: Firas A. Khasawneh

Found 14 papers, 2 papers with code

Topological Signal Processing using the Weighted Ordinal Partition Network

no code implementations27 Apr 2022 Audun Myers, Firas A. Khasawneh, Elizabeth Munch

For this task, we turn to the field of topological data analysis (TDA), which encodes information about the shape and structure of data.

Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform

no code implementations12 Apr 2022 Melih C. Yesilli, Jisheng Chen, Firas A. Khasawneh, Yang Guo

Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.

Texture Classification

Transfer Learning for Autonomous Chatter Detection in Machining

no code implementations11 Apr 2022 Melih C. Yesilli, Firas A. Khasawneh, Brian Mann

Three challenges can be identified in applying machine learning for chatter detection at large in industry: an insufficient understanding of the universality of chatter features across different processes, the need for automating feature extraction, and the existence of limited data for each specific workpiece-machine tool combination.

Time Series Topological Data Analysis +1

Data-driven and Automatic Surface Texture Analysis Using Persistent Homology

no code implementations19 Oct 2021 Melih C. Yesilli, Firas A. Khasawneh

Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis.

Texture Classification Topological Data Analysis

On Transfer Learning of Traditional Frequency and Time Domain Features in Turning

no code implementations28 Aug 2020 Melih C. Yesilli, Firas A. Khasawneh

In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment.

TAG Transfer Learning

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

no code implementations27 Oct 2019 Melih C. Yesilli, Sarah Tymochko, Firas A. Khasawneh, Elizabeth Munch

In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process.

Adaptive Partitioning for Template Functions on Persistence Diagrams

no code implementations18 Oct 2019 Sarah Tymochko, Elizabeth Munch, Firas A. Khasawneh

As the field of Topological Data Analysis continues to show success in theory and in applications, there has been increasing interest in using tools from this field with methods for machine learning.

Topological Data Analysis

Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping

no code implementations5 Aug 2019 Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto

In this paper, we present an alternative approach for chatter detection based on K-Nearest Neighbor (kNN) algorithm for classification and the Dynamic Time Warping (DTW) as a time series similarity measure.

Dynamic Time Warping Time Series +1

Topological Feature Vectors for Chatter Detection in Turning Processes

no code implementations21 May 2019 Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto

We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental turning data.

Time Series

On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition

1 code implementation3 May 2019 Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto

The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting.

General Classification Time Series +1

Persistent Homology of Complex Networks for Dynamic State Detection

no code implementations16 Apr 2019 Audun Myers, Elizabeth Munch, Firas A. Khasawneh

Specifically, we show how persistent homology, a tool from TDA, can be used to yield a compressed, multi-scale representation of the graph that can distinguish between dynamic states such as periodic and chaotic behavior.

Chaotic Dynamics Computational Geometry Information Theory Information Theory Data Analysis, Statistics and Probability

Approximating Continuous Functions on Persistence Diagrams Using Template Functions

2 code implementations19 Feb 2019 Jose A. Perea, Elizabeth Munch, Firas A. Khasawneh

Specifically, we begin by characterizing relative compactness with respect to the bottleneck distance, and then provide explicit theoretical methods for constructing compact-open dense subsets of continuous functions on persistence diagrams.

Time Series Topological Data Analysis

A Look into Chaos Detection through Topological Data Analysis

no code implementations15 Feb 2019 Joshua R. Tempelman, Firas A. Khasawneh

Bi-variate density estimates of the randomly projected time-series in the $p$-$q$ plane described in Gottwald and Melbourne's approach for 0/1 detection are used to generate a gray-scale image.

Chaotic Dynamics

Chatter Classification in Turning Using Machine Learning and Topological Data Analysis

no code implementations23 Mar 2018 Firas A. Khasawneh, Elizabeth Munch, Jose A. Perea

The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods.

General Classification Time Series +1

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