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Tree perception is an essential building block toward autonomous forestry operations. Current developments generally consider input data from lidar sensors to solve forest navigation, tree detection and diameter estimation problems, whereas cameras paired with deep learning algorithms usually address species classification or forest anomaly detection. In either of these cases, data unavailability and forest diversity restrain deep learning developments for autonomous systems. Therefore, we propose two densely annotated image datasets—43 k synthetic, 100 real—for bounding box, segmentation mask and keypoint detections to assess the potential of vision-based methods. Deep neural network models trained on our datasets achieve a precision of 90.4 % for tree detection, 87.2 % for tree segmentation and centimeter accurate keypoint estimations. We measure our models' generalizability when testing it on other forest datasets, and their scalability with different dataset sizes and architectural improvements. Overall, the experimental results offer promising avenues toward autonomous tree felling operations and other applied forestry problems. The datasets and pre-trained models in this article are publicly available on GitHub (https://github.com/norlab-ulaval/PercepTreeV1). [ABSTRACT FROM AUTHOR]

A 7.5-MW wind turbine drivetrain test bench has been used to apply dynamic loads from IEC 61400-1 design load cases to a multi-MW wind turbine drivetrain. A total of 15 test profiles were tested with each test profile demanding the simultaneous application of vertical, lateral, and longitudinal forces, yawing and nodding bending moments, and rotational speed. These inputs to the test bench were compared with the forces, bending moments, and speed that were applied to the wind turbine drivetrain to quantify the tracking error of the test bench and cross-coupling between forces and bending moments. The effect of the tracking error and cross-coupling on the dynamic response of the test article was quantified using multibody simulation. The tracking error was found not to significantly change the dynamic response of the drivetrain. The experimental and simulation results are used to recommend acceptance criteria for the tracking error when replicating dynamic loads. [ABSTRACT FROM AUTHOR]

The exploration of dangerous environments such as underwater coral reefs and shipwrecks is a difficult and potentially life-threatening task for humans, which naturally makes the use of an autonomous robotic system very appealing. This paper presents such an autonomous system, which is capable of autonomous exploration, and shows its use in a series of experiments to collect image data in challenging underwater marine environments. We present novel contributions on three fronts. First, we present an online topic-modeling-based technique to describe what is being observed using a low-dimensional semantic descriptor. This descriptor attempts to be invariant to observations of different corals belonging to the same species, or observations of similar types of rocks observed from different viewpoints. Second, we use the topic descriptor to compute the surprise score of the current observation. This is done by maintaining an online summary of observations thus far, and then computing the surprise score as the distance of the current observation from the summary in the topic space. Finally, we present a novel control strategy for an underwater robot that allows for intelligent traversal, hovering over surprising observations, and swimming quickly over previously seen corals and rocks. [ABSTRACT FROM AUTHOR]

AbstractThis study reports characteristics and outcomes of adults who received Azacitidine-Venetoclax (AZA-VEN) compared to other salvage therapies (NO-AZA-VEN) as first salvage therapy for acute myeloid leukemia (AML). The clinical data of 81 patients with a diagnosis of relapsed or refractory AML were analyzed. The ORR was comparable for both groups (55% vs 57%, p = 0.852). Median OS (6.8 vs 11.2 months, p = 0.053) and median RFS (6.9 vs 11.2 months, p = 0.488) showed a trend in favor of the NO-AZA-VEN group. OS was significantly longer with NO-AZA-VEN for ELN 2022 risk category sub-group, patients under 60 years old, primary AML and for patients who underwent allo-hematopoietic stem cell transplant after salvage therapy. There was no statistical difference in complications of treatment such as febrile neutropenia, intensive care unit stay, septic shock and total parenteral nutrition. Those results do not support the preferential use of AZA-VEN over other regimens in R/R acute myeloid leukemia. [ABSTRACT FROM AUTHOR]