Vessel-to-Vessel Motion Compensation with Reinforcement Learning

Actuation delay poses a challenge for robotic arms and cranes. This is especially the case in dynamic environments where the robot arm or the objects it is trying to manipulate are moved by exogenous forces. In this paper, we consider the task of using a robotic arm to compensate for relative motion between two vessels at sea. We construct a hybrid controller that combines an Inverse Kinematic (IK) solver with a Reinforcement Learning (RL) agent that issues small corrections to the IK input. The solution is empirically evaluated in a simulated environment under several sea states and actuation delays. We observe that more intense waves and larger actuation delays have an adverse effect on the IK controller's ability to compensate for vessel motion. The RL agent is shown to be effective at mitigating large parts of these errors, both in the average case and in the worst case. Its modest requirement for sensory information, combined with the inherent safety in only making small adjustments, also makes it a promising approach for real-world deployment

Development and operational experience of the web based application to collect, manage, and release the alignment and calibration configurations for data processing at CMS

The alignment and calibration workflows at the Compact Muon Solenoid (CMS) experiment are fundamental to provide a high quality physics data and to maintain the design performance of the experiment. To facilitate the operational efforts required by the experiment, the alignment and calibration team has developed and deployed a set of web-based applications to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics, accessible through the Condition DB Browser. The Condition DB Browser hosts also various data management tools, including a visualization tool that allows to easily inspect alignment an calibration contents, an user-defined notification agent for delivering updates on modification to the database, a logging service for the user and the automatic online-to-offline condition uploads. In this paper we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period.Alignment and calibration workflows in CMS require a significant operational effort, due to the complexity of the systems involved. To serve the variety of condition data management needs of the experiment, the alignment and calibration team has developed and deployed a set of web-based applications. The Condition DB Browser is the main portal to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics. It also hosts various data management tools for the conditions including a customized display for certain calibration sets, an automatic user-defined notification agent for updates, a logging service for the user and the automatic online-to-offline uploads. In this presentation we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period

Development and operational experience of the web based application to collect, manage, and release the alignment and calibration configurations for data processing at CMS

The alignment and calibration workflows at the Compact Muon Solenoid (CMS) experiment are fundamental to provide a high quality physics data and to maintain the design performance of the experiment. To facilitate the operational efforts required by the experiment, the alignment and calibration team has developed and deployed a set of web-based applications to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics, accessible through the Condition DB Browser. The Condition DB Browser hosts also various data management tools, including a vi-sualization tool that allows to easily inspect alignment an calibration contents, an user-defined notification agent for delivering updates on modification to the database, a logging service for the user and the automatic online-to-offline condition uploads. In this paper we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period

Airflow limitation as a risk factor for low bone mineral density and hip fracture

Aim: To investigate whether airflow limitation is associated with bone mineral density (BMD) and risk of hip fractures. Methods: A community sample of 5,100 subjects 47–48 and 71–73 years old and living in Bergen was invited. Participants filled in questionnaires and performed a post-bronchodilator spirometry measuring forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC). All attendants were invited to have a BMD measurement of the hip. During 10 years of follow-up, information on death was collected from the Norwegian Cause of Death Registry, and incident hip fractures were registered from regional hospital records of discharge diagnoses and surgical procedure codes. Results: The attendance rate was 69% (n=3,506). The prevalence of chronic obstructive pulmonary disease (COPD) (FEV1/FVC<0.7) was 9%. In multiple logistic regression, the lowest quartile of BMD versus the three upper was significantly predicted by FEV1/FVC<0.7 and FEV1% predicted (odds ratio [OR]: 1.58, 95% confidence interval [CI]: 1.11 to 2.25, and OR per increase of 10%: 0.92, 95% CI: 0.86 to 0.99, respectively). Hip fracture occurred in 126 (4%) participants. In a Cox regression analysis, FEV1% predicted was associated with a lowered risk of hip fracture (hazard ratio per increase of 10%: 0.89, 95% CI: 0.79 to 0.997). Conclusion: Airflow limitation is positively associated with low BMD and risk of hip fracture in middle-aged and elderly

A machine learning classifier for detection of physical activity types and postures during free-living

Introduction: Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity types. This study aimed to evaluate the performance of a machine learning classifier for detecting sitting, standing, lying, walking, running, and cycling based on a dual versus single accelerometer setups during free-living. Methods: Twenty-two adults (mean age [SD, range] 38.7 [14.4, 25–68] years) were wearing two Axivity AX3 accelerometers positioned on the low back and thigh along with a GoPro camera positioned on the chest to record lower body movements during free-living. The labeled videos were used as ground truth for training an eXtreme Gradient Boosting classifier using window lengths of 1, 3, and 5 s. Performance of the classifier was evaluated using leave-one-out cross-validation. Results: Total recording time was ∼38 hr. Based on 5-s windowing, the overall accuracy was 96% for the dual accelerometer setup and 93% and 84% for the single thigh and back accelerometer setups, respectively. The decreased accuracy for the single accelerometer setup was due to a poor precision in detecting lying based on the thigh accelerometer recording (77%) and standing based on the back accelerometer recording (64%). Conclusion: Key daily physical activity types can be accurately detected during free-living based on dual accelerometer recording, using an eXtreme Gradient Boosting classifier. The overall accuracy decreases marginally when predictions are based on single thigh accelerometer recording, but detection of lying is poor

A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living

Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity types. This study aimed to evaluate the performance of a machine learning classifier for detecting sitting, standing, lying, walking, running, and cycling based on a dual versus single accelerometer setups during free-living. Methods: Twenty-two adults (mean age [SD, range] 38.7 [14.4, 25–68] years) were wearing two Axivity AX3 accelerometers positioned on the low back and thigh along with a GoPro camera positioned on the chest to record lower body movements during free-living. The labeled videos were used as ground truth for training an eXtreme Gradient Boosting classifier using window lengths of 1, 3, and 5 s. Performance of the classifier was evaluated using leave-one-out cross-validation. Results: Total recording time was ∼38 hr. Based on 5-s windowing, the overall accuracy was 96% for the dual accelerometer setup and 93% and 84% for the single thigh and back accelerometer setups, respectively. The decreased accuracy for the single accelerometer setup was due to a poor precision in detecting lying based on the thigh accelerometer recording (77%) and standing based on the back accelerometer recording (64%). Conclusion: Key daily physical activity types can be accurately detected during free-living based on dual accelerometer recording, using an eXtreme Gradient Boosting classifier. The overall accuracy decreases marginally when predictions are based on single thigh accelerometer recording, but detection of lying is poor