Path-Following With LiDAR-Based Obstacle Avoidance of an Unmanned Surface Vehicle in Harbor Conditions

This article studies the design, modeling, and implementation challenges of a path-following with obstacle avoidance algorithms as guidance, navigation, and control (GNC) architecture of an unmanned surface vehicle (USV) in harbor conditions. First, an effective mathematical model is developed based on system identification, validating the USV model with field-test data. Then, a guidance system is addressed based on a line-of-sight algorithm, which uses a LiDAR as the main perception sensor for the obstacle avoidance algorithm. The GNC architecture uses a modular approach, including obstacle detection, path-following, and control in the USV platform. Finally, an implementation challenge in two control scenarios, simulation and field test, is addressed to validate the designed GNC architecture.acceptedVersionPeer reviewe

Failure Detection and Isolation by LSTM Autoencoder

Failure diagnosis on some system is often preferred even the data of the system is not designed for the condition monitoring and does not contain any or contains little example cases of failures. For this kind of system, it is unrealistic to directly observe condition from single feature or neither to build a machine learning system that has been trained to detect known failures. Still if any data describing the system exists, it is possible to provide some level of diagnosis on the system. Here we present an LSTM (Long Short Term Memory) autoencoder approach for detecting and isolating system failures with insufficient data conditions. Here we also illustrate how the failure isolation capability is effected by the choice of input feature space. The approach is tested with the flight data of F-18 aircraft and the applicability is validated against several leading edge flap (LEF) control surface seizure failures. The method shows a potential for not only detecting a potential failure in advance but also to isolate the failure by allocating the anomaly on the data to the features that are related to the operation of LEFs. The approach presented here provides diagnostic value from the data than is not designed for condition monitoring neither contain any example case failures.acceptedVersionPeer reviewe

Proceedings of the 1st Annual SMACC Research Seminar 2016

The Annual SMACC Research Seminar is a new forum for researchers from VTT Technical Research Centre of Finland Ltd, Tampere University of Technology (TUT) and industry to present their research on the area of smart machines and manufacturing. The 1st seminar is held on 10th of October 2016 in Tampere, Finland. The objective of the seminar is to publish results of the research to wider audiences and to offer researchers a new forum for discussing methods, outcomes and research challenges of current research projects on SMACC themes and to find common research interests and new research ideas. Smart Machines and Manufacturing Competence Centre - SMACC is joint strategic alliance of VTT Ltd and TUT in the area of intelligent machines and manufacturing. SMACC offers unique services for SME`s in the field of machinery and manufacturing – key features are rapid solutions, cutting-edge research expertise and extensive partnership networks. SMACC is promoting digitalization in mechanical engineering and making scientific research with domestic and international partners in several different topics (www.smacc.fi)

A Graph-Based Model Reduction Method for Digital Twins

Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques have provided new strategies for building complex digital twins. The digital twin is a virtual entity representation of the physical entity, such as a product or a process. This virtual entity is a collection of computationally complex knowledge models that embeds all the information of the physical world. To that end, this article proposes a graph-based representation of the virtual entity. This graph-based representation provides a method to visualize the parameter and their interactions across different modeling domains. However, the virtual entity graph becomes inherently complex with multiple parameters for a complex multidimensional physical system. This research contributes to the body of knowledge with a novel graph-based model reduction method that simplifies the virtual entity analysis. The graph-based model reduction method uses graph structure preserving algorithms and Dempster–Shaffer Theory to provide the importance of the parameters in the virtual entity. The graph-based model reduction method is validated by benchmarking it against the random forest regressor method. The method is tested on a turbo compressor case study. In the future, a method such as graph-based model reduction needs to be integrated with digital twin frameworks to provide digital services by the twin efficiently.Peer reviewe

aColor: Mechatronics, Machine Learning, and Communications in an Unmanned Surface Vehicle

The aim of this work is to offer an overview of the research questions, solutions, and challenges faced by the project aColor ("Autonomous and Collaborative Offshore Robotics"). This initiative incorporates three different research areas, namely, mechatronics, machine learning, and communications. It is implemented in an autonomous offshore multicomponent robotic system having an Unmanned Surface Vehicle (USV) as its main subsystem. Our results across the three areas of work are systematically outlined in this paper by demonstrating the advantages and capabilities of the proposed system for different Guidance, Navigation, and Control missions, as well as for the high-speed and long-range bidirectional connectivity purposes across all autonomous subsystems. Challenges for the future are also identified by this study, thus offering an outline for the next steps of the aColor project.Comment: Paper was originally submitted to and presented in the 8th Transport Research Arena TRA 2020, April 27-30, 2020, Helsinki, Finlan

Rewetting offers rapid climate benefits for tropical and agricultural peatlands but not for forestry‐drained peatlands

Peat soils drained for agriculture and forestry are important sources of carbon dioxide and nitrous oxide. Rewetting effectively reduces these emissions. However, rewetting also increases methane emissions from the soil and, on forestry-drained peatlands, decreases the carbon storage of trees. To analyze the effect of peatland rewetting on the climate, we built radiative forcing scenarios for tropical peat soils, temperate and boreal agricultural peat soils, and temperate and boreal forestry-drained peat soils. The effect of tree and wood product carbon storage in boreal forestry-drained peatlands was also estimated as a case study for Finland. Rewetting of tropical peat soils resulted in immediate cooling. In temperate and boreal agricultural peat soils, the warming effect of methane emissions offsets a major part of the cooling for the first decades after rewetting. In temperate and boreal forestry-drained peat soils, the effect of rewetting was mostly warming for the first decades. In addition, the decrease in tree and wood product carbon storage further delayed the onset of the cooling effect for decades. Global rewetting resulted in increasing climate cooling, reaching -70 mW (m(2)Earth)(-1)in 100 years. Tropical peat soils (9.6 million ha) accounted for approximately two thirds and temperate and boreal agricultural peat soils (13.0 million ha) for one third of the cooling. Forestry-drained peat soils (10.6 million ha) had a negligible effect. We conclude that peatland rewetting is beneficial and important for mitigating climate change, but abandoning tree stands may instead be the best option concerning forestry-drained peatlands.Peer reviewe

Hydroxychloroquine reduces interleukin-6 levels after myocardial infarction : The randomized, double-blind, placebo-controlled OXI pilot trial

Objectives: To determine the anti-inflammatory effect and safety of hydroxychloroquine after acute myocardial infarction. Method: In this multicenter, double-blind, placebo-controlled OXI trial, 125 myocardial infarction patients were randomized at a median of 43 h after hospitalization to receive hydroxychloroquine 300 mg (n = 64) or placebo (n = 61) once daily for 6 months and, followed for an average of 32 months. Laboratory values were measured at baseline, 1, 6, and 12 months. Results: The levels of interleukin-6 (IL-6) were comparable at baseline between study groups (p = 0.18). At six months, the IL-6 levels were lower in the hydroxychloroquine group (p = 0.042, between groups), and in the on-treatment analysis, the difference at this time point was even more pronounced (p = 0.019, respectively). The high-sensitivity C-reactive protein levels did not differ significantly between study groups at any time points. Eleven patients in the hydroxychloroquine group and four in the placebo group had adverse events leading to in-terruption or withdrawal of study medication, none of which was serious (p = 0.10, between groups). Conclusions: In patients with myocardial infarction, hydroxychloroquine reduced IL-6 levels significantly more than did placebo without causing any clinically significant adverse events. A larger randomized clinical trial is warranted to prove the potential ability of hydroxychloroquine to reduce cardiovascular endpoints after myocar-dial infarction. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).Peer reviewe

Genome-wide association study on dimethylarginines reveals novel AGXT2 variants associated with heart rate variability but not with overall mortality

Aims The purpose of this study was to identify novel genetic variants influencing circulating asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) levels and to evaluate whether they have a prognostic value on cardiovascular mortality. Methods and results We conducted a genome-wide association study on the methylarginine traits and investigated the predictive value of the new discovered variants on mortality. Our meta-analyses replicated the previously known locus for ADMA levels in DDAH1 (rs997251; P = 1.4 × 10−40), identified two non-synomyous polymorphisms for SDMA levels in AGXT2 (rs37369; P = 1.4 × 10−40 and rs16899974; P = 1.5 × 10−38) and one in SLC25A45 (rs34400381; P = 2.5 × 10−10). We also fine-mapped the AGXT2 locus for further independent association signals. The two non-synonymous AGXT2 variants independently associated with SDMA levels were also significantly related with short-term heart rate variability (HRV) indices in young adults. The major allele (C) of the novel non-synonymous rs16899974 (V498L) variant associated with decreased SDMA levels and an increase in the ratio between the low- and high-frequency spectral components of HRV (P = 0.00047). Furthermore, the SDMA decreasing allele (G) of the non-synomyous SLC25A45 (R285C) variant was associated with a lower resting mean heart rate during the HRV measurements (P = 0.0046), but not with the HRV indices. None of the studied genome-wide significant variants had any major effect on cardiovascular or total mortality in patients referred for coronary angiography. Conclusions AGXT2 has an important role in SDMA metabolism in humans. AGXT2 may additionally have an unanticipated role in the autonomic nervous system regulation of cardiac functio