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A Real-Time Scheduling Method for Industrial Edge Applications Based on Event Types

A Real-Time Scheduling Method for Industrial Edge Applicatio...

引用

ieee International symposium on Industrial Electronics (ISIE)

作者： Shang Gao Dali Yang Xinkai Zhang Wenbin Dai Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

The development of industrial edge computing brings information and intelligence to traditional industrial control systems. As industrial controllers and edge devices support multiple processor cores, the computing capabilities of field devices have been significantly enhanced. An edge device can run multiple industrial applications simultaneously with different real-time constraints including motion control, image processing, data acquisition, and monitoring. The IEC 61499 standard introduced the event-driven mechanism for industrial control. In this paper, real-time requirements for event types with different deadline attributes are proposed. In addition, scheduling rules and the thread pool-based execution mechanism are investigated to guarantee real-time constraints and determinism. Finally, the proposed method and its performance are evaluated by a case study of a cold-rolled steel simulation production line.

关键词： Job shop scheduling Image edge detection Instruction sets Process control Production Real-time systems Steel

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Deep learning based object detection for agricultural machinery

Deep learning based object detection for agricultural machin...

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International symposium on computational intelligence and Informatics

作者： Sándor Tihamér Brassai András NÉMeth Attila Hammas Szabolcs László GÁBor Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary Intelligent Control Systems Sapientia Hungarian University of Transylvania Corunca Romania Ph.D. in Military Engineering National University of Public Service Budapest Hungary

ISBN: (纸本)9798350398830

Drone imagery based object supervising has become more and more widespread. In the paper the Single shot Alignment Network is used to classify and localize the objects. The images were acquired by using two types of drones, DJI Tello and Zll SG906 Pro 2 in about thirty classes, and about nineteen were processed and detailed in the paper. The objects labeling was realized in CVAT labeling tool. For neural network management the mmdetection framework was used, and the obtained results were detailed on s2a-net. The paper focuses on the preparation of the neural network system to be used for agricultural machine detection. The network was trained on a PC with reduced processing capabilities. The dataset was cut in smaller tasks. An architecture is proposed to be used in future for dataset management during the training process.

关键词： Training Mechatronics Neural networks Object detection Agricultural machinery Labeling Task analysis

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Gradient-Based Online Trajectory Planning for Quadrotor Transportation Systems

Gradient-Based Online Trajectory Planning for Quadrotor Tran...

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ieee International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Tengfei Pei Ying Jin Hai Yu Yongchun Fang Jianda Han Xiao Liang Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin China

Trajectory planning has been an important research area for quadrotor transportation systems for a long time. However, generating feasible trajectories in real-time that allow quadrotor transportation systems to navigate safely in complex environments remains a challenging issue. This article proposes an efficient gradient-based safety constraint that helps keep the system away from obstacles. Furthermore, this paper combines safety constraint with a novel B-spline-based optimization method that utilizes an improved hybrid-state A* algorithm for path searching to generate desired trajectory. Strategies to speed up path searching and unconstrained optimization problem constructed in B-spline optimization guarantee the computational efficiency. The feasibility of the resulting trajectory has been verified through simulation.

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Biased Eavesdropping Particles: A Novel Bio-inspired Heterogeneous Particle Swarm Optimisation Algorithm

Biased Eavesdropping Particles: A Novel Bio-inspired Heterog...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Varna, Fevzi Tugrul Husbands, Phil Univ Sussex Dept Informat Brighton E Sussex England

ISBN: (纸本)9781728190488

The present study proposes a new bio-inspired heterogeneous PSO (particle swarm optimisation) algorithm called BEPSO (biased eavesdropping PSO). The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature, and incorporates a cognitive bias that enables particles to control decisions on cooperation. The algorithm divides the swarm into two distinct particle groups competing to search for a resource. Particles recognise their group members as conspecifics and the other group as heterospecific. When a particle discovers a better position, a contact signal is emitted to attract surrounding conspecifics to the newly discovered position. In nature, multiple species living in the same environment commonly eavesdrop on contact calls originally unintended for them. This can help them to use less energy in searching for resources and potentially increase their fitness. Similarly, in this study, surrounding heterospecifics also eavesdrop on, and exploit, the signal calls originally intended for different conspecific particles. This signaller-recipient interaction experience builds positive or negative bias among particles over time, and particles' final cognitive bias is used as a decision mechanism to exploit signal information. The performance of the proposed algorithm was tested by conducting three distinct experiments on the CEC'17 and CEC'05 benchmark test suites at 30 and 50 dimensions. The results were compared against the results of 12 baseline metaheuristics and 12 state-of-the-art PSO variants. The proposed algorithm outperformed all 24 comparison algorithms in all experiments conducted.

关键词： particle swarm optimisation swarm intelligence

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A parallel intelligent system for optimizing high-speed railway rescheduling by learning 1

A parallel intelligent system for optimizing high-speed rail...

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1st ieee International Conference on Digital Twins and Parallel intelligence, DTPI 2021

作者： Yang, Pengxin Cui, Dongliang Dai, Xuewu Lv, Yisheng Dong, Hairong Wang, Xinghao Liu, Ruiguang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed. © 2021 ieee.

关键词： Intelligent systems

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OTE: Optimal Trustworthy EdgeAI solutions for smart cities 22

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

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22nd ieee/ACM International symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Mygdalis, Vasileios Carnevale, Lorenzo Martinez-de-Dios, Jose Ramiro Shutin, Dmitriy Aiello, Giovanni Villari, Massimo Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece Univ Messina Dept Math & Comp Sci Phys & Hearth Sci Messina Italy Univ Seville Robot Vis & Control Grp Seville Spain German Aerosp Ctr DLR Inst Commun & Nav Wessling Germany Engn Ingn Informat SpA Res & Dev Lab Rome Italy Ist Nazl Alta Matemat INdAM Severi Grp Nazl Calcolo Sci GNCS Rome Italy

ISBN: (纸本)9781665499569

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： EdgeAI Trustworthy-AI Smart city Cloud-Edge-IoT intelligence UAVs

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Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection

Normalized Canonical Correlation Analysis for Calibrating th...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Wei, Chun-Shu Natl Yang Ming Chiao Tung Univ Inst Educ Dept Comp Sci Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn Hsinchu Taiwan

ISBN: (纸本)9781728190488

The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been one of the most stable BCI that is able to transfer commands effectively by the aids of canonical correlation analysis (CCA) for frequency recognition. Nevertheless, CCA-based SSVEP detection encounters the spectral non-uniformity of spontaneous background EEG activity which deteriorates the performance. In this study, we found the performance of SSVEP-based BCI highly predictable by the standard deviation of resting-state response using CCA. Therefore, the proposed normalized CCA (nCCA) aims to calibrate the spectral non-uniformity of background activity which causes bias in classification, and outperforms standard CCA significantly in simulated online tests. In addition, nCCA features near-zero calibration making it a nice fit to practical applications of SSVEP-based BCI spellers.

关键词： Brain-computer interface (BCI) electroencephalography (EEG) steady-state evoked potential (SSVEP) canonical correlation analysis (CCA)

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Invariance-informed reduced-order sparse modeling of partial differential equations with DCA-l1-2 algorithm 34

Invariance-informed reduced-order sparse modeling of partial...

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34th Chinese control and Decision Conference (CCDC)

作者： Li, Yi Li, Xiuting Yue, Zuogong Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430070 Peoples R China

ISBN: (纸本)9781665478960

Learning invariant subspaces is a critical step in the construction of accurate reduced-order models for partial differential equations. In this work, we propose a fully data-driven modeling method for learning the finite-dimensional representations of partial differential equations, by restricting partial differential operator to a linear invariant subspace forming a fundamental set of solutions of an ordinary differential equation. To identify a set of functions forming the linear invariant subspace, we use the proper orthogonal decomposition method to detect the spatial basis functions from data, which sequently results in a reduced dimensional surrogate model. Further, we propose an approach using the sparse learning method to discover the reduced dimensional model from data. Particularly, we formulate the sparse learning problem into a l(1-2) minimization problem and use an iterative computational method based on the difference of convex functions algorithm to solve the optimization problem. We present several numerical tests, including the Schrodinger equation and the semi-linear non-local parabolic equation, to illustrate our approach and to show the effectiveness of the method in comparison to exact solutions.

关键词： Partial differential equations Data-driven Reduced-order modeling Sparse optimization

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Neural Network Modelling of Information Propagation in Social Networks

Neural Network Modelling of Information Propagation in Socia...

引用

International symposium on computational intelligence and Informatics

作者： Győző A. Szilágyi Keleti Károly Faculty of Business and Management Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

In social networks, new information spreads through human connections. The nodes of the network are people, each with a unique opinion about the information. Each person interprets the new information according to his or her own opinion and passes it on, slightly modified and with their opinion incorporated. The aim of the research was to develop an information propagation model that takes into account how new information affects the person receiving the information depending on who he or she receives it from. The model interprets the social network as a complex system of neural networks. By interpreting the social network as a two-level neural network, a network model has been created that makes it easier to build human networks of connections and to predict the extent to which each person’s opinion changes as information is disseminated across the social network.

关键词： Analytical models Mechatronics Social networking (online) computational modeling Neural networks Psychology Predictive models

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Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

Hybrid Evolutionary Approach to Multi-objective Path Plannin...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Hohmann, Nikolas Bujny, Mariusz Adamy, Juergen Olhofer, Markus Tech Univ Darmstadt Control Methods & Robot Lab Darmstadt Germany Honda Res Inst Europe GmbH Offenbach Germany

ISBN: (纸本)9781728190488

The goal of Multi-Objective Path Planning (MOPP) is to find Pareto-optimal paths for autonomous agents with respect to several optimization goals like minimizing risk, path length, travel time, or energy consumption. In this work, we formulate a MOPP for Unmanned Aerial Vehicles (UAVs). We utilize a path representation based on Non-Uniform Rational B-Splines (NURBS) and propose a hybrid evolutionary approach combining an Evolution Strategy (ES) with the exact Dijkstra algorithm. Moreover, we compare our approach in a statistical analysis to state-of-the-art exact (Dijkstra's algorithm), gradient-based (L-BFGS-B), and evolutionary (NSGA-II) algorithms with respect to calculation time and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 2D urban path planning scenario based on real-world data exported from OpenStreetMap. The examination's results indicate that our approach is able to find significantly better solutions for the formulated problem than standard Evolutionary Algorithms (EAs). Moreover, the proposed method is able to obtain more diverse sets of trade-off solutions for different objectives than the standard exact approaches. Thus, the method combines the strengths of both approaches.

关键词： multi-objective optimization path planning hybrid algorithms evolutionary algorithms UAV unmanned aerial vehicle

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