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Distributed delay model of the McKeithan’s network

IFAC-PapersOnLine 2019年第7期52卷 33-38页

作者： György Lipták Katalin M. Hangos Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

In this paper CRNs containing linear reaction chains with multiple joint complexes were considered in order to obtain an equivalent reduced order delayed CRN model with distributed time delays. For this purpose, our earlier method (Lipták and Hangos (2018)) for decomposing the chains of linear reactions with multiple joint complexes was used together with the "linear chain trick". An analytical expression for the kernel function of the distributed delay was also derived from the reaction rate coefficients of the linear reaction chains. Our approach was demonstrated using the example of the well known McKeithan’s network model of kinetic proofreading.

关键词： Process control Delay Chemical Reaction Networks

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Hierarchic neighbors embedding

arXiv

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Corrections to “Outlier-Robust Student's-t-Based IMM-VB Localization for Manned Aircraft Using TDOA Measurements” [Jun 20 1646-1658]

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IEEE/ASME Transactions on Mechatronics 2020年第4期25卷 2139-2139页

作者： Wonkeun Youn Yulong Huang Hyun Myung Korea Aerospace Research Institute Daejeon South Korea Urban Robotics Laboratory School of Electrical Engineering KI-Robotics KI-AI Korea Advanced Institute of Science and Technology Yuseong-gu South Korea Department of Automation Harbin Engineering University Harbin China

Presents corrections to author information for the above named paper.

关键词： Kalman filters Bayes methods Time of arrival estimation Target tracking

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Saturated Continuous Twisting Algorithm 15

Saturated Continuous Twisting Algorithm

引用

15th International Workshop on Variable Structure Systems, VSS 2018

作者： Golkani, M.A. Fridman, L.M. Koch, S. Reichhartinger, M. Horn, M. Faculty of Electrical and Information Engineering Graz University of Technology Institute of Automation and Control Graz Austria Department of Control Engineering and Robotics Division of Electrical Engineering Engineering Faculty National Autonomous University of Mexico Mexico D.F. Mexico Institute of Automation and Control Graz University of Technology Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz Austria

ISBN: (纸本)9781538664391

In this paper, a second-order system affected by perturbations is considered. A feedback control law adopting the continuous twisting algorithm is designed such that a saturated and continuous control signal is introduced to the system. In the case that a state variable is not measurable, estimate information obtained through a super-twisting observer is incorporated into the design. For the closed-loops (with and without observer), global finite-time stability properties are established. The feasibility and effectiveness of the strategy is shown employing numerical simulations. © 2018 IEEE.

关键词： Stability

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Improved deep learning-based macromolecules structure classification from electron cryo-tomograms

Improved deep learning-based macromolecules structure classi...

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作者： Che, Chengqian Lin, Ruogu Zeng, Xiangrui Elmaaroufi, Karim Galeotti, John Xu, Min The Robotics Institute Carnegie Mellon University Pittsburgh United States Department of Automation Tsinghua University Beijing China Computational Biology Department Carnegie Mellon University Pittsburgh United States Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macromolecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image classification

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MuPNet: Multi-modal predictive coding network for place recognition by unsupervised learning of joint visuo-tactile latent representations

arXiv

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arXiv 2019年

作者： Struckmeier, Oliver Tiwari, Kshitij Dora, Shirin Pearson, Martin J. Bohte, Sander M. Pennartz, Cyriel M.A. Kyrki, Ville Department of Electrical Engineering and Automation Aalto University Espoo02150 Finland Cognitive and Systems Neuroscience Group University of Amsterdam Centrum Wiskunde & Informatica Amsterdam Netherlands Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics. Here we present MuPNet (Multi-modal Predictive Coding Network), a biologically plausible network architecture for extracting joint latent features from visuo-tactile sensory data gathered from a biomimetic mobile robot. In this study we evaluate MuPNet applied to place recognition as a simulated biomimetic robot platform explores visually aliased environments. The F1 scores demonstrate that its performance over prior handcrafted sensory feature extraction techniques is equivalent under controlled conditions, with significant improvement when operating in novel environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Network coding

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Cover Image, Volume 6, Issue 10

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Health Science Reports 2023年第10期6卷

作者： Mohammad Faranoush Pooya Faranoush Iraj Heydari Mohammad Reza Foroughi-Gilvaee Azita Azarkeivan Ali Parsai Kia Negin Sadighnia Ali Elahinia Afsoon Zandi Mohammad Reza Rezvany Nahid Hashemi-Madani Amir Ziaee Reza Nekouian Farzaneh Rohani Pediatric Growth and Development Research Center Institute of Endocrinology Iran University of Medical Sciences Tehran Iran Cardio-Oncology Research Center Rajaie Cardiovascular Medical & Research Center Iran University of Medical Sciences Tehran Iran Nano Bio Electronic Devices Lab Cancer Electronics Research Group School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Blood Transfusion Research Center High Institute for Research and Education in Transfusion Medicine Tehran Iran Robotics Research Laboratory School of Mechanical Engineering Iran University of Science and Technology Tehran Iran Department of Otolaryngology Head & Neck Surgery Taleghani Hospital Shahid Beheshti University of Medical Sciences Tehran Iran

The cover image is based on the Research ArticleComplications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional studyby Mohammad Faranoush et al.,https://***/10.1002/hsr2.1624

The cover image is based on the Research Article Complications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional study by Mohammad Faranoush et al., https://***/10.1002/hsr2.1624

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Robust Semi-Global Leaderless Consensus and Containment Control of Identical Linear Systems with Imperfect Actuators

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Journal of Systems Science & Complexity 2018年第1期31卷 69-86页

作者： SHI Liangren ZHAO Zhiyun LIN Zongli Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education Shanghai 200237 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 USA

This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and there input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed to solve these problems. More specifically, it is shown that robust semi-global leaderless consensus can be achieved over a connected undirected graph and robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.

关键词： Consensus containment control dead zone leaderless consensus multi-agent systems saturation

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Variational autoencoded regression: High dimensional regression of visual data on complex manifold

arXiv

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arXiv 2019年

作者： Yoo, YoungJoon Yun, Sangdoo Chang, Hyung Jin Demiris, Yiannis Choi, Jin Young Graduate School of Convergence Science and Technology Seoul National University Korea Republic of ASRI Dept. of Electrical and Computer Eng. Seoul National University Korea Republic of Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London United Kingdom

This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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Vehicular Multi-Camera Sensor System for Automated Visual Inspection of Electric Power Distribution Equipment

Vehicular Multi-Camera Sensor System for Automated Visual In...

引用

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jinsun Park Ukcheol Shin Gyumin Shim Kyungdon Joo Francois Rameau Junhyeok Kim Dong-Geol Choi In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea Korea Electric Power Corporation Korea Electric Power Research Institute Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea

In this paper, we present a multi-camera sensor system along with its control algorithm for automated visual inspection from a moving vehicle. To accomplish this task, we propose a unique hardware configuration consisting of a frontal stereo vision system, six lateral cameras motorized to tilt, and a GPS/IMU sensor mounted on the roof of a car. From the frontal stereo system, we detect electric poles and estimate their corresponding 3D positions. Based on this 3D estimation, the tilt angles of the motorized lateral cameras are controlled in real-time to capture high resolution images of the equipment - typically installed a few meters above the road surface. In addition, inertial odometry information from the GPS/IMU module is utilized for pose estimation, object localization, and re-identification among cameras. Experimental results demonstrate the efficiency and robustness of our system for automated electric equipment maintenance, which can reduce human effort significantly.

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