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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kyu-Seob Song Young-Hoon Nho Dong-Soo Kwon Robotics Program Korea Advanced Institute of Science and Technology Daejeon Korea Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea

This paper proposes a monitoring system to prevent falls from a bed. The position of patient on the bed is categorized as stable and unstable. The system has defined the unstable condition as the situation where a patient is lying on the edge of the bed. The patient was then observed using a thermal imagery camera. We extracted x-, and y-axis histograms that can be used as a feature, using this camera. We used the SVM (Support Vector Machine) to decide an optimal decision boundary and achieved an accuracy of 99.70%.

关键词： Histograms Cameras Support vector machines Monitoring Feature extraction Accelerometers Legged locomotion

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Multi-view 3D Reconstruction in Underwater Environments: Evaluation and Benchmark

Multi-view 3D Reconstruction in Underwater Environments: Eva...

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Oceans (Conference)

作者： Eduardo Iscar Katherine A. Skinner Matthew Johnson-Roberson Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA Robotics Program University of Michigan Ann Arbor MI USA

ISBN: (纸本)9781509064298;9780692946909

Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. In recent years, optical reconstruction methods developed for terrestrial (in air) applications have increasingly been applied to the underwater environment by the marine science community. However, assumptions such as the brightness constancy constraint (BCC) and traditional camera pinhole models, which hold in air, do not apply in the underwater environment. There is a lack of literature about how the violation of these assumptions affects the accuracy of the reconstruction. The main contributions of this paper is to develop a quantitative evaluation of computer vision-based 3D reconstruction methods applied to the underwater domain. Comparisons are presented between low- and high-cost camera systems to quantify the tradeoff between equipment cost and performance.

关键词： habitats surveying submerged performance

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Automatic color correction for 3D reconstruction of underwater scenes

Automatic color correction for 3D reconstruction of underwat...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Katherine A. Skinner Eduardo Iscar Matthew Johnson-Roberson Robotics Program University of Michigan Ann Arbor MI USA Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA

Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. While recent advances in methods for terrestrial mapping can achieve dense 3D reconstructions of scenes in real-time, there remains the challenge of transferring these methods to the underwater domain due to characteristic effects on propagation of light through the water column that violate the brightness constancy constraint used in terrestrial techniques. Current state-of-the-art methods for underwater 3D reconstruction exploit a physical model of light propagation underwater to account for such range-dependent effects as scattering and attenuation; however, these methods necessitate careful calibration of attenuation coefficients required by the physical model, or rely on rough estimates of these coefficients from prior lab experiments. The main contribution of this paper is to develop a novel method to achieve simultaneous estimation of attenuation coefficients for color correction during structure recovery of an underwater scene by integrating this estimation directly into the bundle adjustment step, which performs non-linear optimization. To validate the proposed method, an artificial scene is submerged in a pure water tank and surveyed with a stereo camera platform to simulate an underwater robotic survey in a controlled environment. The target structure is imaged in air with an RGB-D sensor to provide ground truth structure and color, and a color calibration board is place in the scene for further reference. Results show that the proposed method can automatically estimate a water-column aware model for color correction of underwater images simultaneously to 3D reconstruction of the submerged scene.

关键词： Attenuation Three-dimensional displays Cameras Image color analysis Image reconstruction Scattering Solid modeling

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Consistent sparsification for efficient decision making under uncertainty in high dimensional state spaces

Consistent sparsification for efficient decision making unde...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Khen Elimelech Vadim Indelman Robotics and Autonomous Systems Program (TASP) Technion - Israel Institute of Technology Haifa Israel Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa Israel

In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the inference process, the sparsification here is done in the context of efficient decision making, with no impact on the state inference. By identifying state variables which are uninvolved in the decision, we generate a sparse version of the state's information matrix, to be used in the examination of candidate actions. This sparse approximation is action-consistent, i.e. has no influence on the action selection. Overall we manage to maintain the same quality of solution, while reducing the computational complexity of the problem. The approach is put to the test in a SLAM simulation, where a significant improvement in runtime is achieved. Nevertheless, the method is generic, and not tied to a specific type of problem.

关键词： Decision making Sparse matrices Covariance matrices Uncertainty Jacobian matrices Simultaneous localization and mapping

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Effect of injection angle,density ratio,and viscosity on droplet formation in a microfluidic T-junction

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Theoretical & Applied Mechanics Letters 2017年第4期7卷 243-251页

作者： Mohammad Yaghoub Abdollahzadeh Jamalabadi Mohammadreza Daqiq Shirazi Ali Kosar Mostafa Safdari Shadloo Department of Mechanical Robotics and Energy Engineering Dongguk University Seou104620 Republic of Korea School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Mechatronics Engineering Program Faculty of Engineering and Natural Sciences Sabanci University Tuzla Istanbu134956 Turkey CORIA-UMR 6614 Normandie University CNRS-University and INSA of Rouen 76000 Rouen France

The T-junction microchannel device makes available a sharp edge to form micro-droplets from biomaterial solutions. This article investigates the effects of injection angle, flow rate ratio, density ratio,viscosity ratio, contact angle, and slip length in the process of formation of uniform droplets in microfluidic T-junctions. The governing equations were solved by the commercial software. The results show that contact angle, slip length, and injection angles near the perpendicular and parallel conditions have an increasing effect on the diameter of generated droplets, while flow rate, density and viscosity ratios, and other injection angles had a decreasing effect on the diameter.

关键词： Microfluidics Droplet formation Flow rate ratio Density ratio

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Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Driving in the Matrix: Can virtual worlds replace human-gene...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Matthew Johnson-Roberson Charles Barto Rounak Mehta Sharath Nittur Sridhar Karl Rosaen Ram Vasudevan Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Robotics Program University of Michigan Ann Arbor MI USA Mechanical Engineering University of Michigan Ann Arbor MI USA

Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.

关键词： Engines Training Machine learning Data models Robots Training data Automobiles

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images

arXiv

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arXiv 2017年

作者： Li, Jie Skinner, Katherine A. Eustice, Ryan M. Johnson-Roberson, Matthew Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Robotics Program University of Michigan Ann ArborMI48109 United States Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States

This paper reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high resolution images to map the seafloor;however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects such as absorption and scattering, which cause attenuation of light at different rates for different wavelengths. While this physical process is well described theoretically, the model depends on many parameters intrinsic to the water column as well as the structure of the scene. These factors make recovery of these parameters difficult without simplifying assumptions or field calibration;hence, restoration of underwater images is a non-trivial problem. Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in deep sea environments. Using WaterGAN, we generate a large training dataset of corresponding depth, in-air color images, and realistic underwater images. This data serves as input to a two-stage network for color correction of monocular underwater images. Our proposed pipeline is validated with testing on real data collected from both a pure water test tank and from underwater surveys collected in the field. Source code, sample datasets, and pretrained models are made publicly available. Copyright © 2017, The Authors. All rights reserved.

关键词： Image reconstruction

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Interactions between marine megafauna and plastic pollution in Southeast Asia

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Science of the Total Environment 2023年 874卷 162502页

作者： Omeyer, Lucy C.M. Duncan, Emily M. Abreo, Neil Angelo S. Acebes, Jo Marie V. AngSinco-Jimenez, Lea A. Anuar, Sabiqah T. Aragones, Lemnuel V. Araujo, Gonzalo Carrasco, Luis R. Chua, Marcus A.H. Cordova, Muhammad R. Dewanti, Lantun P. Espiritu, Emilyn Q. Garay, Jovanie B. Germanov, Elitza S. Getliff, Jade Horcajo-Berna, Eva Ibrahim, Yusof S. Jaafar, Zeehan Janairo, Jose Isagani B. Gyi, Thanda Ko Kreb, Danielle Lim, Cheng Ling Lyons, Youna Mustika, Putu L.K. Neo, Mei Lin Ng, Sirius Z.H. Pasaribu, Buntora Pariatamby, Agamuthu Peter, Cindy Porter, Lindsay Purba, Noir P. Santa Cruz, Ernesto T. Shams, Shahriar Thompson, Kirsten F. Torres, Daniel S. Westerlaken, Rodney Wongtawan, Tuempong Godley, Brendan J. Centre for Ecology and Conservation University of Exeter Penryn Campus Penryn CornwallTR10 9EZ United Kingdom Institute of Marine Sciences-Okeanos University of the Azores Rua Professor Doutor Frederico Machado 4 Horta9901-862 Portugal AI and Robotics Laboratory-Environmental Studies University of the Philippines Mindanao Philippines *** Bohol Jagna Philippines Zoology Division The National Museum of the Philippines Padre Burgos Avenue Manila Philippines Davao Oriental City of Mati Philippines Faculty of Science and Marine Environment Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Marine Mammal Research & Conservation Laboratory Institute of Environmental Science and Meteorology College of Science University of the Philippines Diliman Quezon City Philippines Marine Research and Conservation Foundation Lydeard St Lawrence Somerset United Kingdom Environmental Science Program Department of Biological and Environmental Sciences College of Arts and Sciences Qatar University Doha Qatar Department of Biological Sciences National University of Singapore 14 Science Drive 4 117543 Singapore Lee Kong Chian Natural History Museum National University of Singapore 2 Conservatory Drive Singapore117377 Singapore BRIN Kawasan Jakarta Ancol Jalan Pasir Putih 1 Ancol Timur Jakarta14430 Indonesia Fishery Department Faculty of Fishery and Marine Science Universitas Padjadjaran Bandung40600 Indonesia Department of Environmental Science Ateneo de Manila University Loyola Heights Quezon City1108 Philippines San Isidro Extension Campus Davao Oriental San Isidro Philippines Marine Megafauna Foundation West Palm BeachFL United States Centre for Sustainable Aquatic Ecosystems Harry Butler Institute Murdoch University PerthWA Australia Environmental and Conservation Sciences Murdoch University PerthWA Australia Roctopus ecoTrust Roctopus Dive Sairee Beach Koh Tao 84360 Thailand Juara Turtle Project Kg Juara Pahang Tioman Islan

Southeast (SE) Asia is a highly biodiverse region, yet it is also estimated to cumulatively contribute a third of the total global marine plastic pollution. This threat is known to have adverse impacts on marine megafauna, however, understanding of its impacts has recently been highlighted as a priority for research in the region. To address this knowledge gap, a structured literature review was conducted for species of cartilaginous fishes, marine mammals, marine reptiles, and seabirds present in SE Asia, collating cases on a global scale to allow for comparison, coupled with a regional expert elicitation to gather additional published and grey literature cases which would have been omitted during the structured literature review. Of the 380 marine megafauna species present in SE Asia, but also studied elsewhere, we found that 9.1 % and 4.5 % of all publications documenting plastic entanglement (n = 55) and ingestion (n = 291) were conducted in SE Asian countries. At the species level, published cases of entanglement from SE Asian countries were available for 10 % or less of species within each taxonomic group. Additionally, published ingestion cases were available primarily for marine mammals and were lacking entirely for seabirds in the region. The regional expert elicitation led to entanglement and ingestion cases from SE Asian countries being documented in 10 and 15 additional species respectively, highlighting the utility of a broader approach to data synthesis. While the scale of the plastic pollution in SE Asia is of particular concern for marine ecosystems, knowledge of its interactions and impacts on marine megafauna lags behind other areas of the world, even after the inclusion of a regional expert elicitation. Additional funding to help collate baseline data are critically needed to inform policy and solutions towards limiting the interactions of marine megafauna and plastic pollution in SE Asia. © 2023 The Authors

关键词： Ecosystems

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A study on robust control method of underwater vehicle using SMCSPO

A study on robust control method of underwater vehicle using...

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Asian Control Conference

作者： Hyun Hee Kim Jong Seob Won Hyeon Jin Cho Jun Ho Hwang Min Cheol Lee Interdisciplinary Program in Robotics Pusan National University Geumjeong-gu Busan Korea Department of Mechanical and Automotive Engineering Jeonju University Wansan-gu Jeonju-si Jeollabuk-do Korea Agency for Defense Development Korea School of Mechanical Engineering Pusan National University Geumjeong-gu Busan Korea

ISBN: (纸本)9781509015740;9781509015733

The application of robust control method is required for the design of the automatic control system of the unmanned underwater vehicle which is operated in underwater environment under great disturbance. Sliding Perturbation Observer (SPO) was developed to overcome the chattering phenomenon which is a weak point of the sliding mode control. This control method can be applied to the control of underwater vehicle operating in strong environmental disturbances because it has the advantage of estimating and compensating for nonlinear components, modeling uncertainties and environmental disturbances of the system. In this study, the sliding mode control with sliding perturbation observer (SMCSPO) is simulated in an underwater vehicle to evaluate the disturbance compensation performance and compare it with a PID controller.

关键词： Underwater vehicles Perturbation methods Vehicle dynamics Observers Mathematical model Sliding mode control PI control

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