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arXiv 2022年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden and Digital Futures StockholmSE-100 44 Sweden Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node’s estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples. © 2022, CC BY.

关键词： Directed graphs

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Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization

arXiv

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arXiv 2023年

作者： Irofti, Paul Romero-Ben, Luis Stoican, Florin Puig, Vicenç Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest 010014 Romania The Institute for Logic and Data Science Popa Tatu 18 Bucharest010805 Romania The Institut de Robòtica i Informàtica Industrial CSIC-UPC Llorens i Artigas 4-6 Barcelona08028 Spain The Universitat Politècnica de Catalunya Campus de Terrassa Gaia Building Rambla Sant Nebridi 22 Terrassa Barcelona08222 Spain The Deptartment of Automation Control and Systems Engineering Politehnica University of Bucharest 060042 Romania

This article presents a leak localization methodology based on state estimation and learning. The first is handled by an interpolation scheme, whereas dictionary learning is considered for the second stage. The novel proposed interpolation technique exploits the physics of the interconnections between hydraulic heads of neighboring nodes in water distribution networks. Additionally, residuals are directly interpolated instead of hydraulic head values. The results of applying the proposed method to a well-known case study (Modena) demonstrated the improvements of the new interpolation method with respect to a state-of-the-art approach, both in terms of interpolation error (considering state and residual estimation) and posterior localization. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Event-based Quality-of-Service Parameter Estimation of a Wireless Channel

Event-based Quality-of-Service Parameter Estimation of a Wir...

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International Conference on Event-based control, Communication, and Signal Processing (EBCCSP)

作者： Michael Schwung Yasemin Karacora Jan Lunze Aydin Sezgin Institute of Automation and Computer Control Ruhr University Bochum Germany Institute of Digital Communication Systems Ruhr University Bochum Germany

ISBN: (数字)9781728195810

ISBN: (纸本)9781728195827

This paper proposes an event-based method to estimate the quality-of-service (QoS) parameters of a wireless communication channel between two unmanned aerial vehicles (UAV). A network estimator determines the QoS parameters at an event time by a channel estimation. Between two event times a model of the channel provides an estimate of the current QoS parameters. A two-state Markov model, which is updated at event times, models the properties of the physical communication channel, which are varying with the distance between the UAVs. In contrast to existing literature the QoS parameters of a varying channel between objects are estimated continuously by a channel model and are utilised for an event-based collision avoidance method, which was introduced in previous publications to cope with an unreliable network. A simulation study with two quadrotors shows the accuracy of the estimate and that collisions can only be avoided by considering the channel properties.

关键词： Channel estimation Quality of service Trajectory Collision avoidance Wireless communication Packet loss Markov processes

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Tang, Ying Sun, Shengtao Wu, Ben Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process. © 2020 IEEE.

关键词： Image reconstruction

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The role of interpretable patterns in deep learning for morphology

arXiv

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arXiv 2020年

作者： Ács, Judit Kornai, András Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary

We examine the role of character patterns in three tasks: morphological analysis, lemmatization and copy. We use a modified version of the standard sequence-to-sequence model, where the encoder is a pattern matching network. Each pattern scores all possible N character long subwords (substrings) on the source side, and the highest scoring subword’s score is used to initialize the decoder as well as the input to the attention mechanism. This method allows learning which subwords of the input are important for generating the output. By training the models on the same source but different target, we can compare what subwords are important for different tasks and how they relate to each other. We define a similarity metric, a generalized form of the Jaccard similarity, and assign a similarity score to each pair of the three tasks that work on the same source but may differ in target. We examine how these three tasks are related to each other in 12 languages. Our code is publicly available. © 2020, CC BY-SA.

关键词： Pattern matching

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Experimental Evaluation of an Adaptive Cruise control and Cooperative Merging Concept

Experimental Evaluation of an Adaptive Cruise Control and Co...

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control Technology and Applications (CCTA),

作者： Alexander Schwab Lisa-Marie Reichelt Philipp Welz Jan Lunze Institute of Automation and Computer Control Ruhr-Universität Bochum Ruhr-Universität Bochum Bochum Germany

ISBN: (数字)9781728171401

ISBN: (纸本)9781728171418

This paper presents an experimental evaluation of a concept for platooning and cooperative merging of networked vehicles in case of an upcoming lane reduction. In order to test the platooning and merging concept, differential-driven robots are used. Low level path controllers are designed to guide the robots to a prescribed path on which the robots can be represented by linear models. Furthermore, a lane changing approach will be presented that maintains the longitudinal inter-robot distance while moving laterally to the other lane. The effectiveness of the proposed methods is discussed with experimental results.

关键词： Robot kinematics Merging Collision avoidance Cruise control Robot sensing systems Mobile robots

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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Synchronizing sequences for verification of finite state machines

Synchronizing sequences for verification of finite state mac...

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2019 International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2019

作者： Mroshnyk, Maryna Shkil, Olexander Kulak, Elvira Rakhlis, Dariia Pahomov, Yurii Mroshnyk, Anatolii Dept. Specialized Computer Systems Ukrainian State University of Railway Transport Kharkiv Ukraine Dept. Computer Engineering Design Kharkiv National University of Radio Electronics Kharkiv Ukraine Dept. Automation and Control in Technical Systems of the National Technical University Kharkov Polytechnic Institute Kharkiv Ukraine

ISBN: (纸本)9781728126920

The method of detection and localization of design errors in HDL-models of finite state machines with arbitrary output functions was proposed. The diagnostic experiment is carried out bypassing all arcs of the Mealy machine, starting from the initial vertex, including for machines of the "nonexclusive" class. To ensure the return of the machine with a possible design error in the initial state, it is suggested to use synchronizing sequences. Diagnostic experiments were performed in the Active-HDL design environment. © 2019 IEEE

关键词： Diagnistic experiment HDL State diagram State machine Tests Verification Waveform

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Self-supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data

Self-supervised Detection and Pose Estimation of Logistical ...

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International Conference on Pattern Recognition

作者： Nikolas Müller Jonas Stenzel Jian-Jia Chen Plan-Based Robot Control German Research Center for Artificial Intelligence (DFKI) Osnabrück Germany Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Institute of Computer Science Design Automation for Embedded Systems Technical University of Dortmund Dortmund Germany

Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and only few publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. The solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional voting network and the computation of the pose for each detected object instance.

关键词： Location awareness Solid modeling Three-dimensional displays Pose estimation Robot vision systems Training data Object detection

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