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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

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Solving the Time-Optimal Control Problem for Nonlinear Non-Autonomous Linearizable Systems

SSRN

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SSRN 2023年

作者： Sklyar, Katerina V. Ignatovich, Svetlana Yu. Sklyar, Grigory M. Department of Automatics and Robotics West Pomeranian University of Technology str. 26 Kwietnia 10 Szczecin71-126 Poland Department of Applied Mathematics V.N. Karazin Kharkiv National University Svobody sqr. 4 Kharkiv61022 Ukraine Faculty of Computer Science and Information Technology West Pomeranian University of Technology str. Zolnierska 49 Szczecin71-210 Poland Institute of Mathematics University of Szczecin Wielkopolska 15 Szczecin70-451 Poland

We present conditions under which the time-optimal control problem for a nonlinear non-autonomous linearizable system can be solved by the method of successive approximations, at each step of which a power Markov moment minproblem is solved. The proposed method can be efficiently implemented by use of symbolic and numerical calculations. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs

arXiv

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arXiv 2022年

作者： Kong, He Sukkarieh, Salah Arnold, Travis J. Chen, Tianshi Mu, Biqiang Zheng, Wei Xing Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China Sydney Institute for Robotics and Intelligent Systems The University of Sydney NSW2006 Australia Madison WI53704 United States School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as Q and R, respectively) of systems with unknown inputs. This paper considers the identifiability of Q/R using the correlation-based measurement difference approach. More specifically, we establish (i) necessary conditions under which Q and R can be uniquely jointly identified;(ii) necessary and sufficient conditions under which Q can be uniquely identified, when R is known;(iii) necessary conditions under which R can be uniquely identified, when Q is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus. © 2022, CC BY.

关键词： Stochastic systems

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INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE BEHAVIOR POLICIES 8

INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE ...

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8th International Conference on Learning Representations, ICLR 2020

作者： Chen, Xinyun Wang, Lu Hang, Yizhe Ge, Heng Zha, Hongyuan Insitute for Data and Decision Analytics The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Computer Science East China Normal University China Department of Computer Science University of Science and Technology of China China School of Mathematics and Statistics Shandong University China Georgia Institute of Technology United States College of Computing Georgia Institute of Technology United States

We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies. Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation. We propose estimated mixture policy (EMP), a partially policy-agnostic methods to accurately estimate those quantities. With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction. In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

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Comparative Analysis of Two Simulation Environments for Robots, Gazebo, and CoppeliaSim in the Context of Their Use for Teaching Students a Course in Robotic Systems Modeling

Comparative Analysis of Two Simulation Environments for Robo...

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IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Fatimat M. Tseeva Mariyana M. Shogenova Khamisha M. Senov Kanukoeva V. Liana Asiyat M. Bozieva Institute of Computer Science Electronics and Robotics Federal State Budgetary Educational Institution of Higher Education «Kabardino-Balkarian State University named after H.M. Berbekov» Nalchik Russia Federation Institute of Architecture Construction and Design Federal State Budgetary Educational Institution of Higher Education «Kabardino-Balkarian State University named after H.M. Berbekov» Nalchik Russia Federation Institute of Phisics and Mathematics Federal State Budgetary Educational Institution of Higher Education «Kabardino-Balkarian State University named after H.M. Berbekov» Nalchik Russia Federation

ISBN: (数字)9798350367522

ISBN: (纸本)9798350367539

This article presents a comparative analysis of two simulation environments for robots and robotic systems - Gazebo and CoppeliaSim. The study evaluates their functionality and effectiveness for teaching students robot and robotic system modeling. The analysis was conducted based on the following criteria: user interface convenience, availability of educational materials, simulation realism, support for various types of robots and sensors, and integration capabilities with other software. As a result, our research allowed us to identify the main advantages and disadvantages of each simulation environment. These findings will help determine the most suitable tool for robotic education, thereby improving the quality of training for professionals in this field.

关键词： Training Analytical models Adaptation models Virtual environments User interfaces Systems modeling Writing Robot sensing systems Sensors Testing

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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PROVAL: A framework for comparison of protein sequence embeddings

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Journal of Computational mathematics and Data science 2022年 3卷

作者： Väth, Philipp Münch, Maximilian Raab, Christoph Schleif, F.-M. Center for Artificial Intelligence and Robotics University for Applied Sciences Würzburg-Schweinfurt Sanderheinrichsleitenweg 20 Würzburg Germany Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen P.O. Box 407 Groningen NL-9700 AK Netherlands

High throughput sequencing technology leads to a significant increase in the number of generated protein sequences and the anchor database UniProt doubles approximately every two years. This large set of annotated data is used by many bioinformatics algorithms. Searching within these databases, typically without using any annotations, is challenging due to the variable lengths of the entries and the used non-standard comparison measures. A promising strategy to address these issues is to find fixed-length, information-preserving representations of the variable length protein sequences. A systematic algorithmic evaluation of the proposals is however surprisingly missing. In this work, we analyze how different algorithms perform in generating general protein sequence representations and provide a thorough evaluation framework PROVAL. The strategies range from a proximity representation using classical Smith–Waterman algorithm to state-of-the-art embedding techniques by means of transformer networks. The methods are evaluated by, e.g., the molecular function classification, embedding space visualization, computational complexity and the carbon footprint. © 2022 The Author(s)

关键词： Deep learning Dissimilarity representation Protein sequence embeddings Representation learning Smith–Waterman

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Semiparametric modeling of the spatiotemporal trends in natural gas consumption: Methodology, results, and consequences

Semiparametric modeling of the spatiotemporal trends in natu...

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作者： Brabec, Marek Konár, Ondřej Kasanický, Ivan Malý, Marek Pelikán, Emil Institute of Computer Science The Czech Academy of Sciences Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic Faculty of Mathematics and Physics Department of Probability and Mathematical Statistics Charles University in Prague Prague Czech Republic

Household consumption of natural gas is usually considered to be quite stable as cooking, space, and water heating belong to basic needs. The improvement of technologies together with possibilities of switching to alternative sources can, however, lead to a decreasing consumption trend. Knowing more about such trend, especially of its spatial distribution, can be useful for strategic planning. In this paper, we describe a general statistical methodology allowing to study the spatiotemporal behavior of consumption. It is based on semiparametric modeling. Formalized error and sensitivity analyses are part of the methodology. Presented methods are illustrated on large-scale data from the Czech Republic. © 2019 John Wiley & Sons, Ltd.

关键词： Natural gas

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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 2022年第4期60卷 589-590页

作者： F van den Noort C Manzini C H van der Vaart M A J van Limbeek C H Slump A T M Grob Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. Department of Obstetrics and Gynecology University Medical Centre Utrecht Utrecht The Netherlands. Dynamics of Complex Fluids Max Planck Institute for Dynamics and Self-Organization Göttingen Germany. Multi-Modality Medical Imaging Faculty of Science and Technology Technical Medical Centre University of Twente Enschede The Netherlands.

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