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1. NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

   https://doi.org/10.1088/1741-2552/ad593e  

   Li, Yi; Frederick, Rebecca_A; George, Daniel; Cogan, Stuart_F; Pancrazio, Joseph_J; Bleris, Leonidas; Hernandez-Reynoso, Ana_G (June 2024, Journal of Neural Engineering)

   Abstract Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation.Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using akvalue of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%.Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models. 

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2. Verification and validation of dielectric mapping technique for non‐destructive evaluation of polymer matrix composites

   https://doi.org/10.1002/pc.28848  

   Berkowitz, Katherine; Guha, Rishabh_D; Oluwajire, Oluwatimilehin; Grace, Landon_R (August 2024, Polymer Composites)

   Abstract The rapid increase in use of polymer matrix composites in different industries underscores the need for reliable non‐destructive evaluation techniques to characterize small‐scale damage and prevent structural failure. A novel dielectric technique exploits moisture‐polymer interactions to identify and track damage, leveraging differences in dielectric properties between free and bound water. This technique has demonstrated the ability to detect low levels of damage, but the localization accuracy has not yet been evaluated. This work utilizes unsupervised machine learning to assess the technique's ability to identify the damage boundary following a low‐velocity impact event. Bismaleimide/quartz and E‐glass/epoxy laminates were impacted via drop tower to induce varying levels of damage, and subsequently inspected via dielectric technique at several moisture levels by weight. Resulting data was processed via k‐means clustering and the identified damage boundary was compared to a boundary obtained from backlit images and scanning electron microscopy. Accuracy was quantified using developed metrics for damage centroid and boundary identification. The technique averaged 93.9% accuracy in determining the damage center and 77.5% accuracy in identifying the damage boundary. Results indicated the technique's effectiveness across varying moisture levels, particularly in damage centroid identification. Localization accuracy was shown to be insensitive to moisture content, improving the technique's practical capabilities. Further analysis revealed potential for delineation of delaminations. HighlightsLow‐velocity impact of two material architectures.Damage boundary determined and validated via scanning electron microscopy.Detected damage site via dielectric technique compared to damage boundary.High technique accuracy revealed; >90% centroid localization accuracy.Potential for delamination delineation observed. 

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3. Transform Waveforms into Signature Vectors for General-purpose Incipient Fault Detection

   https://doi.org/10.1109/TPWRD.2022.3151110  

   Weng, Yang; Cui, Qiushi; Guo, Muhao (February 2022, IEEE Transactions on Power Delivery)

   Power system equipment presents special signatures at the incipient stage of faults. As more renewables are integrated into the systems, these signatures are harder to detect. If faults are detected at an early stage, economical losses and power outages can be avoided in modern power grids. Many researchers and power engineers have proposed a series of signature-specific methods for one type of equipment's waveform abnormality. However, conventional methods are not designed to identify multiple types of incipient faults (IFs) signatures at the same time. Therefore, we develop a general-purpose IF detection method that detects waveform abnormality stemming from multiple types of devices. To avoid the computational burden of the general-purpose IF detection method, we embed the abnormality signatures into a vector and develop a pre-training model (PTM) for machine understanding. In the PTM, signal "words," "sentences," and "dictionaries" are designed and proposed. Through the comparison with a machine learning classifier and a simple probabilistic language model, the results show a superior detection performance and reveal that the training radius is highly related to the size of abnormal waveforms. 

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4. Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning

   https://doi.org/10.3390/app13031935  

   Ardani, Samira; Eftekhar Azam, Saeed; Linzell, Daniel G. (February 2023, Applied Sciences)

   This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population. 

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5. Progress in developing a bark beetle identification tool

   https://doi.org/10.1371/journal.pone.0310716  

   Marais, G Christopher; Stratton, Isabelle C; Johnson, Andrew J; Hulcr, Jiri (June 2025, PLOS One)

   Kajtoch, Łukasz (Ed.)

   This study presents an initial model for bark beetle identification, serving as a foundational step toward developing a fully functional and practical identification tool. Bark beetles are known for extensive damage to forests globally, as well as for uniform and homoplastic morphology which poses identification challenges. Utilizing a MaxViT-based deep learning backbone which utilizes local and global attention to classify bark beetles down to the genus level from images containing multiple beetles. The methodology involves a process of image collection, preparation, and model training, leveraging pre-classified beetle species to ensure accuracy and reliability. The model's F1 score estimates of 0.99 and 1.0 indicates a strong ability to accurately classify genera in the collected data, including those previously unknown to the model. This makes it a valuable first step towards building a tool for applications in forest management and ecological research. While the current model distinguishes among 12 genera, further refinement and additional data will be necessary to achieve reliable species-level identification, which is particularly important for detecting new invasive species. Despite the controlled conditions of image collection and potential challenges in real-world application, this study provides the first model capable of identifying the bark beetle genera, and by far the largest training set of images for any comparable insect group. We also designed a function that reports if a species appears to be unknown. Further research is suggested to enhance the model's generalization capabilities and scalability, emphasizing the integration of advanced machine learning techniques for improved species classification and the detection of invasive or undescribed species. 

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