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Automation, control and Robotics Engineering (CACRE), International Conference on

作者： Liangliang Wang Guanglei Huo Xinwei Chen Chengxi Huang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology Quanzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot – Xiaozhi II verify its robustness.

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Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction

Collaborative Visual-Inertial Localization of Teams With Flo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Sándor Gazdag Dániel Pasztornicky Zsolt Jankó Tamás Szirányi András L. Majdik Institute for Computer Science and Control (SZTAKI) Machine Perception Research Laboratory Budapest Kende u. 13.-17 Department of Material Handling and Logistics Systems Budapest University of Technology and Economics Budapest Bertalan Lajos u. 7

This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data collected by the agents moving around the building to automatically generate the building’s floorplan on which the agent trajectories are overlaid. The wearable hardware consists of a low-cost passive integrated sensor that includes both a camera and an IMU (Inertial Measurement Unit) and an embedded compute unit. The system’s capabilities are shown through real-world experiments.

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection

Heterogeneous Graph Convolutional Network for Visual Reinfor...

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Automation, control and Robotics Engineering (CACRE), International Conference on

作者： Liangliang Wang Chengxi Huang Xinwei Chen School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology Quanzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method.

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

Augmenting GRIPS with Heuristic Sampling for Planning Feasib...

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European Conference on Mobile Robots (ECMR)

作者： Brian Angulo Ivan Radionov Konstantin Yakovlev Moscow Institute of Physics and Technology LLC "Integrant" Federal Research Center "Computer Science and Control" RAS

Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) [1] is a recently introduced method for such transformation. GRIPS iteratively deforms the path and adds/deletes the waypoints while trying to connect each consecutive pair of them via the provided steering function that respects the kinematic constraints. The algorithm is relatively fast but, unfortunately, does not provide any guarantees that it will succeed. In practice, it often fails to produce feasible trajectories for car-like robots with large turning radius. In this work, we introduce a range of modifications that are aimed at increasing the success rate of GRIPS for car-like robots. The main enhancement is adding an additional step that heuristically samples the waypoints along the bottleneck parts of the geometric paths (such as sharp turns). The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.

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The Method of Optimal Geometric Parameters Synthesis of Two Mechanisms in the Rehabilitation System on Account of Relative Position 12th

The Method of Optimal Geometric Parameters Synthesis of Tw...

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12th International Conference on Optimization and Applications, OPTIMA 2021

作者： Malyshev, Dmitry Rybak, Larisa Mohan, Santhakumar Diveev, Askhat Cherkasov, Vladislav Pisarenko, Anton Belgorod State Technological University named after V.G. Shukhov Belgorod Russia Indian Institute of Technology Palakkad Palakkad India Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030927103

The paper presents the method of optimal geometric parameters synthesis for a robotic rehabilitation system of the lower limbs as based on a passive orthosis in the form of a serial RRRR mechanism and an active parallel 3-PRRR mechanism. A mathematical model of the kinematics of the RRRR mechanism is formulated. A numerical method is worked out for determining the workspace of the RRRR mechanism. The workspace description is based on the approximation of many solutions of systems of nonlinear inequalities to provide the required rotation angle of rotation in the joints. Effective numerical methods and algorithms are developed and tested. The paves the way for determining the minimum geometric parameters of an active parallel mechanism that ensure movements of a passive orthosis within the workspace under clinical data constrains when simulating walking. In order to implement the developed methods, an effective algorithm, software package and visualization system are synthesized for exported three-dimensional workspaces in STL. © 2021, Springer Nature Switzerland AG.

关键词： Mechanisms

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Investigating Role of Renewable Resources Including Wind Turbines in Energy Hub by Considering Simultaneous Demand for Electricity, Heating and Cooling

Investigating Role of Renewable Resources Including Wind Tur...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani Gevork B. Gharehpetian Department of Physics and Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

As a crucial component of multi-energy systems (MES), the energy hub significantly enhances their performance and reliability. In the energy system, the utilization of renewable energy sources (RES) can presents a significant optimization problem, capable of substantially reducing environmental pollution and lowering energy costs for users. This paper presents an optimal load dispatch model incorporating a collection of wind turbines, aimed at reducing the overall cost of operating an energy hub. It is also proposes a hub structure based on wind, natural gas, and electricity as a combined heat and power system, utilizing converters and energy storage mechanisms to obtain electricity, thermal, and cooling energy. To accomplish this, two situations are executed in the network, aiming to minimize costs by applying a problem-solving approach to the installation of wind turbines at an energy hub. After implementing energy hub management in the optimal mode, the system’s operating costs are reduced by $16 \%$, demonstrating its advantage.

关键词： Renewable energy sources Costs Electricity Wind turbines Problem-solving Trigeneration Optimization

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Model Predictive control with Reach-avoid Analysis

arXiv

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arXiv 2023年

作者： Ren, Dejin Lu, Wanli Lv, Jidong Zhang, Lijun Xue, Bai State Key Lab. of Computer Science Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Detecting spatio-temporal Relations by Combining a Semantic Map with a Stream Processing Engine

Detecting spatio-temporal Relations by Combining a Semantic ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lennart Niecksch Henning Deeken Thomas Wiemann German Research Center for Artificial Intelligence (DFKI) DFKI Niedersachsen Plan-based Robot Control Group Osnabrück Germany Knowledge Based Systems and Autonomous Robotics groups Institute of Computer Science Osnabrück University Osnabrück Germany

Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively researched, the reasoning about the temporal development of spatial relations of instances is often neglected. This paper presents a concept to combine a semantic map with a stream processing framework for live analysis of the spatio-temporal relation of objects, based on the map and information inferred from sensors streams. To demonstrate the functionality of our concept, we implemented a proof-of-concept system to track everyday events in an office environment. The presented application scenario clearly demonstrates the benefits of the proposed architecture for detecting and handling complex spatio-temporal events.

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Parameter estimation of T1DM models with a particular focus on endogenous glucose production

Parameter estimation of T1DM models with a particular focus ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Siket, Mate Toth, Rebeka Rudas, Imre Eigner, Gyorgy Kovacs, Levente Physiological Controls Research Center Óbuda University Hungary Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Óbuda University Hungary Applied Informatics and Applied Mathematics Doctoral School Óbuda University Bécsi street 96/B. BudapestH-1034 Hungary Institute for Computer Science and Control Kende utca 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The effects of individual physiological phenomena play an important role considering the accuracy of artificial pancreas systems. An example of these phenomena is the heart rate which is easy to measure. There is a connection between heart rate and endogenous glucose production that significantly influences the blood glucose level. The proper implementation of heart rate could lead to defining physical activity in TIDM models. The aim of the current study is to examine how the change in endogenous glucose production influences the fitting accuracies in model versions with different complexity. Our extensions include a heart rate dependent endogenous glucose production equation, modeling the effect of physical activity, and a part defining the effect of insulin on endogenous glucose production. The joint effect of the mentioned extensions was also considered. © 2022 IEEE.

关键词： Heart

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