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IEEE International Conference on Image Processing

作者： Ziyang Zheng Ziliang Ren Zhanhao Liang Gulin Wang Qieshi Zhang School of Computer Science and Technology Dongguan University of Technology China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Automatic Control Department Kyrgyz State Technical University named after Iskhak Razzakov Bishkek Kyrgyzstan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Human motion prediction (HMP) refers to predicting the future body pose from the historical pose sequence. Many existing methods use Graph Convolutional Networks (GCN) to model the human body and convert the human pose from the pose space to the trajectory space or 3D coordinates. Furthermore, GCN treat human poses as a generic graph formed by links between each pair of body joints to encode the dependence of human spatial poses as well as temporal information by working in trajectory space. We design a multi-stage distributed processing network that includes Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). The multistage strategy enables us to gradually acquire smoother inputs. Additionally, we have incorporated an attention mechanism within the processing framework, which helps T-DGCN better capture temporal dependencies. As a result, the proposed network not only facilitates more effective feature extraction but also achieves state-of-the-art performance on the CMU-Mocap and 3DPW datasets. Our code is available at https://***/ihavenotgoodname/MSGAT.

关键词： Convolutional codes Solid modeling Distributed processing Three-dimensional displays Attention mechanisms Graph convolutional networks Feature extraction

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Traffic Sign Classification Using Deep and Quantum Neural Networks

arXiv

引用

arXiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2022, CC BY.

关键词： Traffic signs

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Cyber Physical Systems Architecture for Collaborative Services 23

Cyber Physical Systems Architecture for Collaborative Servic...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Pop, Eugen Gifu, Daniela Stanescu, Aurelian Mihai Moisescu, Mihnea Alexandru Sacala, Ioan Stefan RandD Institute for Automation IPA SA ICT Department Bucharest Romania Alexandru Ioan Cuza University of Iasi Institute of Computer Science Romanian Academy Faculty of Computer Science Iasi Branch Romania Politehnica University of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9781665439398

Cyber Physical Systems (CPS) represents an autonomous system which integrates sensing devices, actuators, hardware equipments and software applications, having also communication functionality. A CPS can realize data transmission and cooperation with remote systems and IoT networks. Within smart factories, having the ability of involving business scenarios, CPS autonomously exchange data, control and monitor processes, perform actions in the Industry 4.0 context. Correlated with CPS interoperability capabilities, a new paradigm in manufacturing appeared, based on collaborative, service-oriented, customer-centric industries. Various new business models involve a large, exponentially growing number, of interconnected devices. In this paper is presented a CPS services framework, which integrates an e-business service group within a collaborative infrastructure, thus offering to the users the possibility to valorify on the market, through services integration and collaboration, the remarkable technical advantages of CPS. As e-business example, an e-payment service, developed in a Service Oriented Architecture (SOA), is considered. The data transactions are realized on mobile and wireless communication channels, through Soap and Rest Web services, which are accessed by mobile client software application, installed on Android OS Smartphones. © 2021 IEEE.

关键词： Cyber Physical System

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On the Existence and Uniqueness of Steady State Solutions of a Class of Dynamic Hydraulic Networks via Actuator Placement

On the Existence and Uniqueness of Steady State Solutions of...

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IEEE Conference on Decision and control

作者： Mark Jeeninga Juan E. Machado Michele Cucuzzella Giacomo Como Jacquelien Scherpen Department of Automatic Control Lund University Lund Sweden Chair of Control Systems and Network Control Technology Faculty 3 Mechanical Engineering Electrical and Energy Systems Brandenburg University of Technology Cottbus - Senftenberg Cottbus Germany Department of Electrical Computer Science and Biomedical Engineering Univeristy of Pavia Pavia Italy Department of Mathematical Science “G.L. Lagrange” Politecnico di Torino Torino Italy Department of Automatic Control Lund University Faculty of Science and Engineering Jan C. Willems Center for Systems and Control ENTEG University of Groningen Groningen the Netherlands

In this paper, using tools from graph theory we provide verifiable necessary and sufficient conditions for the existence of a unique hydraulic equilibrium in district heating systems of meshed topology and containing multiple heat sources. Even though numerous publications have addressed the design of efficient algorithms for numerically finding hydraulic equilibria in the general context of water distribution networks, this is not the case for the analysis of existence and uniqueness. Moreover, most of the existing work dealing with these aspects exploit the equivalence between the nonlinear algebraic equations describing the hydraulic equilibria and the KKT conditions of a suitably defined nonlinear convex optimization problem. Differently, this paper proposes necessary and sufficient graph-theoretic conditions on the actuator placement for the existence and uniqueness of a hydraulic equilibrium, independent of the actuators' control objective. An example based on a representative district heating network is considered to illustrate the key aspects of our contribution, and an explicit formulation of the steady state solution is given for the case in which pressure drops through pipes are linear with respect to the flow rate.

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BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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IEEE International Conference on Big Data

作者： Hassan Eldeeb Shota Amashukeli Radwa ElShawi Institute of Computer Science University of Tartu Tartu Estonia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt.

ISBN: (纸本)9781665480468

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.

关键词： Measurement Industries Scalability Pipelines Buildings Machine learning Big Data

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On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

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Traffic Sign Classification Using Deep and Quantum Neural Networks

TechRxiv

引用

TechRxiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

关键词： Traffic signs

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On the use of dictionary learning in time series imputation 28

On the use of dictionary learning in time series imputation

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Zheng, Xiaomeng Dumitrescu, Bogdan Liu, Jiamou Giurcaneanu, Ciprian Doru Department of Statistics University of Auckland Auckland New Zealand Department of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9789082797053

In this work, we show how dictionary learning (DL) can be employed in the imputation of univariate and multivariate time series. In the multivariate case, we propose to use a structured dictionary. The size of the dictionary and the sparsity level are selected by information theoretic criteria. We also evaluate the effect of removing the trend/seasonality before applying DL. We conduct an extensive experimental study on real-life data. The positions of the missing data are simulated by applying two strategies: (i) sampling without replacement, which leads to isolated occurrences of the missing data, and (ii) sampling via Polya urn model that is likely to produce long sequences of missing data. In all scenarios, the novel DL-based methods compare favorably with the state-of-the-art. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Time series

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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A markerless high resolution structural health monitoring framework for smart cities

A markerless high resolution structural health monitoring fr...

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2021 IEEE Technology and Engineering Management Conference - Europe, TEMSCON-EUR 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Ferworn, Alexander Chaoyang University of Technology Department of Information Management Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Ryerson University Department of Computer Science Toronto Canada

ISBN: (纸本)9781665440912

Our paper introduces a novel structural health monitoring (SHM) framework for preexisting surveillance camera video footage towards an automated structural engineering e-governance system in a smart city. We test our framework on a sample pole structure using a high-resolution camera and an off-the-shelf phone camera. A preliminary study suggests the efficacy of using the framework in monitoring noticeable degradation and aging in large structures in periodically captured images. Our framework is dissimilar to computer vision techniques in which deformation patterns are recognized;instead, our framework is purposed as a long term observation application in which large structures in public video surveillance footage is monitored for changes that may suggest signs of aging or degradation of a structure over a long period of time. We posit that this novel framework, with emerging technology and innovation, can pave the way to combine artificial intelligence and smart structural health monitoring techniques in a widespread, unprecedented way of ensuring safe public structures in smart cities. © 2021 IEEE.

关键词： Smart city

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