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A multi-agent architecture for scheduling in platform-based smart manufacturing systems

Frontiers of Information Technology & Electronic engineering 2019年第11期20卷 1465-1492页

作者： Yong-kui LIU Xue-song ZHANG Lin ZHANG Fei TAO Li-hui WANG Center for Intelligent Manufacturing Systems and Robots School of Mechano-Electronic EngineeringXidian UniversityXi'an 710071China College of Computer Science and Technology Jilin UniversityChangchun 130012China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Production Engineering KTH Royal Institute of TechnologyStockholm 11428Sweden

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for sched-uling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

关键词： Platform Smart manufacturing Multi-agent Scheduling

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Machine learning in the air

arXiv

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arXiv 2019年

作者： Gundüz, Deniz De Kerret, Paul Sidiropoulos, Nicholas D. Gesbert, David Murthy, Chandra Van Der Schaar, Mihaela Information Processing and Communications Laboratory Department of Electrical and Electronics Engineering Imperial College London London United Kingdom Communication Systems Department EURECOM Sophia Antipolis France Department of Electrical and Computer Engineering University of Virginia Charlottesville VA United States Department of Electrical and Computer Engineering Indian Institute of Science Bangalore India

Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications. Copyright © 2019, The Authors. All rights reserved.

关键词： Intelligent buildings

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Baseball pitch type recognition based on broadcast videos 3rd

Baseball pitch type recognition based on broadcast videos

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3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Chen, Reed Siegler, Dylan Fasko, Michael Yang, Shunkun Luo, Xiong Zhao, Wenbing Duke University DurhamNC27708 United States Georgia Institute of Technology North Avenue AtlantaGA30332 United States Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States School of Reliability and Systems Engineering Beihang University 37 Xueyuan Road Beijing100191 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789811519246

In this paper, we report our work on baseball pitch type recognition based on broadcast videos using two-stream inflated 3D convolutional neural network (I3D). To improve the state-of-the-art of research, we developed our own high-quality dataset, trained and tuned the I3D model extensively, primarily combating the problem of overfitting while still trying to improve final validation accuracy. In the end, we are able to achieve an accuracy of 53.43% ± 3.04% when oversampling and 57.10% ± 2.99% when not oversampling, which is a significant improvement over the published best result of an accuracy of 36.4% on the same six pitch type classes. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Support vector machines

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Erratum to “A Nonlinear Receiver Leveraging Cascaded Inverter-Based Envelope-Biased LNAs for In-Band Interference Suppression in the Amplitude Domain” [Nov 21 3360-3374]

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IEEE Journal of Solid-State Circuits 2022年第2期57卷 671-671页

作者： Dawei Ye Rongjin Xu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University Shanghai China Institute of Brain-Inspired Circuits and Systems (iBiCAS) Fudan University Shanghai China MOE Frontiers Center for Brain Science Fudan University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle WA USA

In the above article [1] , Fig. 4 is wrong. The correct one is shown below:

关键词： Interference suppression Internet of Things CMOS analog integrated circuits Low-noise amplifiers

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Guest Editorial Special Issue on Affordances

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IEEE Transactions on Cognitive and Developmental systems 2018年第1期10卷 1-3页

作者： Jamone, Lorenzo Ugur, Emre Santos-Victor, José ARQ-Advanced Robotics at Queen Mary School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Computer Engineering Department Boǧaziçi University Istanbul Turkey Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

The concept of affordances appeared in psychology during the late 60s, as an alternative perspective on the visual perception of the environment. More precisely, the term affordances was introduced by J. J. Gibson in 1966 [items 1)-3) of the Appendix]. Gibson defines the affordances of an object as “what it offers the animal, what it provides or furnishes, either for good or ill.” What the objects afford to the agent are action possibilities that are directly perceived through vision, in a precategorical and subconscious way, without the need to construct a fully detailed model of the world or to perform semantic reasoning or explicit object recognition. Central to Gibson’s theory is the notion that the sensorimotor capabilities of the agent dramatically influence perception: the concept of affordances is “something that refers to both the environment and the animal in a way that no existing term does. It implies the complementarity of the animal and the environment.” Notably, the human ability to perceive affordances emerges gradually during development, and it is the outcome of exploratory and observational learning; as this ability appears, predicting the effects of the actions becomes possible as well, eventually leading to problem solving skills.

关键词： Special issues and sections Psychology Robot sensing systems Cognition Biological system modeling

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Network inference from consensus dynamics with unknown parameters

arXiv

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arXiv 2019年

作者： Zhu, Yu Schaub, Michael T. Jadbabaie, Ali Segarra, Santiago Department of Electrical and Computer Engineering Rice University United States Institute for Data Systems and Society MIT United States Department of Engineering Science University of Oxford United Kingdom

We explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamics

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Sentilyzer: Aspect-oriented sentiment analysis of product reviews

Sentilyzer: Aspect-oriented sentiment analysis of product re...

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2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

作者： Wladislav, Schulz Johannes, Zenkert Christian, Weber Andre, Klahold Madjid, Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

ISBN: (纸本)9781728113609

Aspect-oriented sentiment analysis is used in particular on textual product reviews to identify positive or negative product characteristics. This information is beneficial not only for customers related to purchasing decisions, but also used by manufacturers to improve their products. In this short paper, we introduce Sentilyzer, a system which performs state-of-the-art aspect-oriented sentiment analysis based on automatically generated category-specific sentiment and aspect dictionaries from Amazon product reviews. © 2018 IEEE.

关键词： Sentiment analysis

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Extended harmonic analysis of wireless charging systems 44

Extended harmonic analysis of wireless charging systems

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44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Sankar, Arun Mallik, Ayan Khaligh, Alireza Electrical and Computer Engineering Department United States Institute for Systems Research University of Maryland College Park United States

ISBN: (纸本)9781509066841

This paper presents a novel mathematical modeling and analysis technique for inductive wireless charging systems. Typically, LC resonance is the most common and basic physical phenomena enabling inductive power transfer (IPT). The work in this paper describes the limitations and inaccuracy of the fundamental harmonic approximation (FHA), which is widely used in the analysis of any resonant based compensation network. Our work introduces a new comprehensive analysis approach for modeling an IPT system, i.e. extended harmonics analysis (EHA), which accounts for the effects of multiple other odd order harmonics on the power transfer, voltage gain, voltage and current stresses on the devices;and hence, provides more accurate design guidelines. As verification of this concept, a laboratory prototype of the wireless charging system is built and tested up to 1 kW and analyzed using both FHA and EHA. It is shown that the deviation of the current estimates from the experimental measurements using EHA is reduced to ~1% from a maximum deviation of ~11% obtained from FHA. Also, the use of EHA modeling has reduced the deviation from experimentally measured voltage gain to 9.6% from 28% in the case of FHA modeling. © 2018 IEEE.

关键词： Harmonic analysis

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Uncertainties and interdependence of porous parameters using Bayesian model-based analysis 48

Uncertainties and interdependence of porous parameters using...

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48th International Congress and Exhibition on Noise Control engineering, INTER-NOISE 2019 MADRID

作者： Ritchie, Kyle Xiang, Ning Horoshenkov, Kirill Hurrell, Alistair Department of Electrical Computer and Systems Engineering Rensselear Polytechnic Institute 110 8th St TroyNY12180 United States Graduate Program in Architectural Acoustics Rensselear Polytechnic Institute 110 8th St TroyNY12180 United States Department of Mechanical Engineering University of Sheffield Western Bank SheffieldS10 2TN United Kingdom

ISBN: (纸本)9788487985317

Traditional modeling for sound propagation in porous media has been done knowing the macroscopic physical parameters intrinsic to the material. Using Bayesian model-based analysis the uncertainties of these parameters and their effect on the accuracy of the selected model can be studied. The Horoshenkov Model [Horoshenkov, et al., JASA vol 139 (2016)], or similarly Miki Model [Miki, JASJ, vol 11, pp.25-28 (1990)], can be used to estimate the mean pore size and porosity of the material under test from measurements of surface impedance or absorption coefficient using glass beads of known diameter and layer thickness as an experimental model. The accuracy of these models can be compared using quantitative measures such as mean, variance and the interdependence of the estimated parameters. This paper discusses the error and likelihood of several models using the experimentally measured data sets. © INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control engineering. All Rights Reserved.

关键词： Porous materials

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Semi-supervised medical image classification with relation-driven self-ensembling model

arXiv

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arXiv 2020年

作者： Liu, Quande Yu, Lequan Luo, Luyang Dou, Qi Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States T Stone Robotics Institute Chinese University of Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data. We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets, i.e., skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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