Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Unsupervised machine learning for flaw detection in automated ultrasonic testing of carbon fibre reinforced plastic composites.

The use of Carbon Fibre Reinforced Plastic (CFRP) composite materials for critical components has significantly surged within the energy and aerospace industry. With this rapid increase in deployment, reliable post-manufacturing Non-Destructive Evaluation (NDE) is critical for verifying the mechanical integrity of manufactured components. To this end, an automated Ultrasonic Testing (UT) NDE process delivered by an industrial manipulator was developed, greatly increasing the measurement speed, repeatability, and locational precision, while increasing the throughput of data generated by the selected NDE modality. Data interpretation of UT signals presents a current bottleneck, as it is still predominantly performed manually in industrial settings. To reduce the interpretation time and minimise human error, this paper presents a two-stage automated NDE evaluation pipeline consisting of a) an intelligent gating process and b) an autoencoder (AE) defect detector. Both stages are based on an unsupervised method, leveraging density-based spatial clustering of applications with noise clustering method for robust automated gating and undefective UT data for the training of the AE architecture. The AE network trained on ultrasonic B-scan data was tested for performance on a set of reference CFRP samples with embedded and manufactured defects. The developed model is rapid during inference, processing over 2000 ultrasonic B-scans in 1.26 s with the area under the receiver operating characteristic curve of 0.922 in simple and 0.879 in complex geometry samples. The benefits and shortcomings of the presented methods are discussed, and uncertainties associated with the reported results are evaluated.

Widespread support for a global species list with a formal governance system.

Taxonomic data are a scientific common. Unlike nomenclature, which has strong governance institutions, there are currently no generally accepted governance institutions for the compilation of taxonomic data into an accepted global list. This gap results in challenges for conservation, ecological research, policymaking, international trade, and other areas of scientific and societal importance. Consensus on a global list and its management requires effective governance and standards, including agreed mechanisms for choosing among competing taxonomies and partial lists. However, governance frameworks are currently lacking, and a call for governance in 2017 generated critical responses. Any governance system to which compliance is voluntary requires a high level of legitimacy and credibility among those by and for whom it is created. Legitimacy and credibility, in turn, require adequate and credible consultation. Here, we report on the results of a global survey of taxonomists, scientists from other disciplines, and users of taxonomy designed to assess views and test ideas for a new system of taxonomic list governance. We found a surprisingly high degree of agreement on the need for a global list of accepted species and their names, and consistent views on what such a list should provide to users and how it should be governed. The survey suggests that consensus on a mechanism to create, manage, and govern a single widely accepted list of all the world's species is achievable. This finding was unexpected given past controversies about the merits of list governance.

Tightening the requirements for species diagnoses would help integrate DNA-based descriptions in taxonomic practice.

Modern advances in DNA sequencing hold the promise of facilitating descriptions of new organisms at ever finer precision but have come with challenges as the major Codes of bionomenclature contain poorly defined requirements for species and subspecies diagnoses (henceforth, species diagnoses), which is particularly problematic for DNA-based taxonomy. We, the commissioners of the International Commission on Zoological Nomenclature, advocate a tightening of the definition of "species diagnosis" in future editions of Codes of bionomenclature, for example, through the introduction of requirements for specific information on the character states of differentiating traits in comparison with similar species. Such new provisions would enhance taxonomic standards and ensure that all diagnoses, including DNA-based ones, contain adequate taxonomic context. Our recommendations are intended to spur discussion among biologists, as broad community consensus is critical ahead of the implementation of new editions of the International Code of Zoological Nomenclature and other Codes of bionomenclature.

Interpretable & Explainable Machine Learning for Ultrasonic Defect Sizing.

Despite its popularity in literature, there are few examples of machine learning (ML) being used for industrial nondestructive evaluation (NDE) applications. A significant barrier is the 'black box' nature of most ML algorithms. This paper aims to improve the interpretability and explainability of ML for ultrasonic NDE by presenting a novel dimensionality reduction method: Gaussian feature approximation (GFA). GFA involves fitting a 2D elliptical Gaussian function an ultrasonic image and storing the seven parameters that describe each Gaussian. These seven parameters can then be used as inputs to data analysis methods such as the defect sizing neural network presented in this paper. GFA is applied to ultrasonic defect sizing for inline pipe inspection as an example application. This approach is compared to sizing with the same neural network, and two other dimensionality reduction methods (the parameters of 6 dB drop boxes and principal component analysis), as well as a convolutional neural network applied to raw ultrasonic images. Of the dimensionality reduction methods tested, GFA features produce the closest sizing accuracy to sizing from the raw images, with only a 23% increase in RMSE, despite a 96.5% reduction in the dimensionality of the input data. Implementing ML with GFA is implicitly more interpretable than doing so with principal component analysis or raw images as inputs, and gives significantly more sizing accuracy than 6 dB drop boxes. Shapley additive explanations (SHAP) are used to calculate how each feature contributes to the prediction of an individual defect's length. Analysis of SHAP values demonstrates that the GFA-based neural network proposed displays many of the same relationships between defect indications and their predicted size as occur in traditional NDE sizing methods.

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization.

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ( R=0.84 ) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ( R=0.95 ) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ( R=0.98 ) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples.

A new species of Trimma (Pisces: Gobiidae) from the deep reefs of Palau, western Pacific Ocean.

A new species of Trimma is described from three specimens from deep reefs (91.4 m) at Uchelbeluu Reef, Palau, western Pacific Ocean. Trimma panemorfum n. sp. is characterized by a live colouration of a yellow to orange body with two light blue stripes, each with a ventral bar of the same colour from the anterior origin. The predorsal midline is scaled, opercular and cheek scales are absent, the middle 1213 pectoral-fin rays are branched, the fifth pelvic-fin ray has two dichotomous branch points (total of four branch tips), the bony interorbital is 3442% pupil width and does not extend ventrolaterally beyond the fifth papilla of row p, where the posterior interorbital trench is present as a slight groove or absent.

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data.

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1-5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm.

The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.

Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all-important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine-grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half-Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half-Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.

Deep Learning for Ultrasonic Crack Characterization in NDE.

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.

Principles for creating a single authoritative list of the world's species.

Lists of species underpin many fields of human endeavour, but there are currently no universally accepted principles for deciding which biological species should be accepted when there are alternative taxonomic treatments (and, by extension, which scientific names should be applied to those species). As improvements in information technology make it easier to communicate, access, and aggregate biodiversity information, there is a need for a framework that helps taxonomists and the users of taxonomy decide which taxa and names should be used by society whilst continuing to encourage taxonomic research that leads to new species discoveries, new knowledge of species relationships, and the refinement of existing species concepts. Here, we present 10 principles that can underpin such a governance framework, namely (i) the species list must be based on science and free from nontaxonomic considerations and interference, (ii) governance of the species list must aim for community support and use, (iii) all decisions about list composition must be transparent, (iv) the governance of validated lists of species is separate from the governance of the names of taxa, (v) governance of lists of accepted species must not constrain academic freedom, (vi) the set of criteria considered sufficient to recognise species boundaries may appropriately vary between different taxonomic groups but should be consistent when possible, (vii) a global list must balance conflicting needs for currency and stability by having archived versions, (viii) contributors need appropriate recognition, (ix) list content should be traceable, and (x) a global listing process needs both to encompass global diversity and to accommodate local knowledge of that diversity. We conclude by outlining issues that must be resolved if such a system of taxonomic list governance and a unified list of accepted scientific names generated are to be universally adopted.

Mesophotic.org: a repository for scientific information on mesophotic ecosystems.

Mesophotic coral ecosystems (MCEs) and temperate mesophotic ecosystems (TMEs) occur at depths of roughly 30-150 m depth and are characterized by the presence of photosynthetic organisms despite reduced light availability. Exploration of these ecosystems dates back several decades, but our knowledge remained extremely limited until about a decade ago, when a renewed interest resulted in the establishment of a rapidly growing research community. Here, we present the 'mesophotic.org' database, a comprehensive and curated repository of scientific literature on mesophotic ecosystems. Through both manually curated and automatically extracted metadata, the repository facilitates rapid retrieval of available information about particular topics (e.g. taxa or geographic regions), exploration of spatial/temporal trends in research and identification of knowledge gaps. The repository can be queried to comprehensively obtain available data to address large-scale questions and guide future research directions. Overall, the 'mesophotic.org' repository provides an independent and open-source platform for the ever-growing research community working on MCEs and TMEs to collate and expedite our understanding of the occurrence, composition and functioning of these ecosystems. Database URL: http://mesophotic.org/.

Three new species of Chromis (Teleostei, Pomacentridae) from mesophotic coral ecosystems of the Philippines.

Three new species of Chromis (Perciformes, Pomacentridae) from the Philippines, collected between 75-150 m depth, are described by a combination of morphological features and their coloration. Chromisgunting sp. n. was found in Batangas and Oriental Mindoro, and differs from its congeners in body depth (2.1-2.2 in SL), and color of adults, light brown, with a silver area on the anterior end and a bilateral black margin along the exterior side of the tail. It is most similar to C.scotochiloptera, with a 5.3% genetic divergence in COI. Chromishangganan sp. n. was found around Lubang Island. Body depth (1.9-2.0 in SL) and adult coloration (yellowish with dark black outer margins on dorsal and anal fins) also separate this species from its congeners. It is most similar to C.pembae, with a 2.5% genetic divergence. Chromisbowesi sp. n. was found in Batangas, and also differs from its congeners by the combination of body depth (1.5-1.6 in SL), and color of adults (brownish grey in the dorsal side to whitish on the ventral side, with alternating dark and light stripes in the sides of body). It is most similar to C.earina, with a 3.6% genetic divergence in COI.

Prognathodesgeminus, a new species of butterflyfish (Teleostei, Chaetodontidae) from Palau.

A new species of the butterflyfish genus Prognathodes (Chaetodontidae) is described from two specimens collected at a depth of 116 m off Ngemelis Island, Palau. Prognathodesgeminus sp. n. is similar to P.basabei Pyle & Kosaki, 2016 from the Hawaiian archipelago, and P.guezei (Maugé & Bauchot, 1976) from the western Indian Ocean, but differs from these species in the number of soft dorsal-fin rays, size of head, body width, and body depth. There are also subtle differences in life color, and substantial differences in the mtDNA cytochrome oxidase I sequence (d ≈ 0.08). Although genetic comparisons with P.guezei are unavailable, it is expected that the genetic divergence between P.guezei and P.geminus will be even greater than that between P.geminus and P.basabei. It is named for the strikingly similar color pattern it shares with P.basabei.

Tosanoidesannepatrice, a new basslet from deep coral reefs in Micronesia (Perciformes, Percoidei, Serranidae).

The new species Tosanoidesannepatrice sp. n. is described from four specimens collected at depths of 115-148 m near Palau and Pohnpei in Micronesia. It differs from the other three species of this genus in life color and in certain morphological characters, such as body depth, snout length, anterior three dorsal-fin spine lengths, caudal-fin length, and other characters. There are also genetic differences from the other four species of Tosanoides (d ≈ 0.04-0.12 in mtDNA cytochrome oxidase I). This species is presently known only from Palau and Pohnpei within Micronesia, but it likely occurs elsewhere throughout the tropical western Pacific.

Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs.

The rapid degradation of coral reefs is one of the most serious biodiversity problems facing our generation. Mesophotic coral reefs (at depths of 30 to 150 meters) have been widely hypothesized to provide refuge from natural and anthropogenic impacts, a promise for the survival of shallow reefs. The potential role of mesophotic reefs as universal refuges is often highlighted in reef conservation research. This hypothesis rests on two assumptions: (i) that there is considerable overlap in species composition and connectivity between shallow and deep populations and (ii) that deep reefs are less susceptible to anthropogenic and natural impacts than their shallower counterparts. Here we present evidence contradicting these assumptions and argue that mesophotic reefs are distinct, impacted, and in as much need of protection as shallow coral reefs.

A comprehensive investigation of mesophotic coral ecosystems in the Hawaiian Archipelago.

Although the existence of coral-reef habitats at depths to 165 m in tropical regions has been known for decades, the richness, diversity, and ecological importance of mesophotic coral ecosystems (MCEs) has only recently become widely acknowledged. During an interdisciplinary effort spanning more than two decades, we characterized the most expansive MCEs ever recorded, with vast macroalgal communities and areas of 100% coral cover between depths of 50-90 m extending for tens of km2 in the Hawaiian Archipelago. We used a variety of sensors and techniques to establish geophysical characteristics. Biodiversity patterns were established from visual and video observations and collected specimens obtained from submersible, remotely operated vehicles and mixed-gas SCUBA and rebreather dives. Population dynamics based on age, growth and fecundity estimates of selected fish species were obtained from laser-videogrammetry, specimens, and otolith preparations. Trophic dynamics were determined using carbon and nitrogen stable isotopic analyses on more than 750 reef fishes. MCEs are associated with clear water and suitable substrate. In comparison to shallow reefs in the Hawaiian Archipelago, inhabitants of MCEs have lower total diversity, harbor new and unique species, and have higher rates of endemism in fishes. Fish species present in shallow and mesophotic depths have similar population and trophic (except benthic invertivores) structures and high genetic connectivity with lower fecundity at mesophotic depths. MCEs in Hawai'i are widespread but associated with specific geophysical characteristics. High genetic, ecological and trophic connectivity establish the potential for MCEs to serve as refugia for some species, but our results question the premise that MCEs are more resilient than shallow reefs. We found that endemism within MCEs increases with depth, and our results do not support suggestions of a global faunal break at 60 m. Our findings enhance the scientific foundations for conservation and management of MCEs, and provide a template for future interdisciplinary research on MCEs worldwide.