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Abstract In recent years, advances in image processing and machine learning have fueled a paradigm shift in detecting genomic regions under natural selection. Early machine learning techniques employed population-genetic summary statistics as features, which focus on specific genomic patterns expected by adaptive and neutral processes. Though such engineered features are important when training data are limited, the ease at which simulated data can now be generated has led to the recent development of approaches that take in image representations of haplotype alignments and automatically extract important features using convolutional neural networks. Digital image processing methods termed α-molecules are a class of techniques for multiscale representation of objects that can extract a diverse set of features from images. One such α-molecule method, termed wavelet decomposition, lends greater control over high-frequency components of images. Another α-molecule method, termed curvelet decomposition, is an extension of the wavelet concept that considers events occurring along curves within images. We show that application of these α-molecule techniques to extract features from image representations of haplotype alignments yield high true positive rate and accuracy to detect hard and soft selective sweep signatures from genomic data with both linear and nonlinear machine learning classifiers. Moreover, we find that such models are easy to visualize and interpret, with performance rivaling those of contemporary deep learning approaches for detecting sweeps.
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A Vision-Based Health Inspection of Power Line Conductors for the Mobile Damping Robot
Abstract Ensuring the structural integrity of the overhead power line conductor is crucial for maintaining the safety and reliability of the electrical transmission system. Exposure to environmental hazards like moisture, dust, and Wind-Induced Vibrations (WIV) can lead to defects and corrosion in power line conductors, which are primary contributors to fatigue and shortened lifespan. Thus, this paper presents a vision-based health inspection of power line conductors for a maintenance robot. The method involves image filtering techniques such as Sobel, Scharr, and Gray-scale Variance Normalization (GVN). After filtering the image, row and column analysis is conducted to identify relevant patterns that distinguish healthy and unhealthy conductors, utilizing histograms for data representation. From the histogram data analysis, 10 features were chosen from observation. Subsequently, the collected image data is classified into either healthy or unhealthy categories through supervised machine learning models, including Random Forest (RF), Multi-Layer Perception (MLP), and Gradient Boosting (GB). The best combination of features is extracted to optimize each machine-learning models accordingly. Experimental results validated the effectiveness of our method, which has been specifically fitted for the Mobile Damping Robot (MDR), presenting its potential for enhancing power line maintenance.
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- Award ID(s):
- 2038187
- PAR ID:
- 10587897
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8835-3
- Format(s):
- Medium: X
- Location:
- Washington, DC, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2024-146393
