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A relax-and-fix heuristic approach for the capacitated dynamic lot sizing problem in integrated manufacturing/remanufacturing systems

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IFAC-PapersOnLine 2017年第1期50卷 9008-9013页

作者： Roshani A. Giglio D. Paolucci M. Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Via Opera Pia 13 Genova 16145 Italy Department of Mechanical Energy Management and Transportation Engineering (DIME) University of Genova Via Opera Pia 15 Genova 16145 Italy

In this paper, the capacitated dynamic lot sizing problem in integrated manufac-turing/remanufacturing systems is addressed. These kinds of production systems are designed to satisfy the demands of different classes of single-level products not only by manufacturing raw materials, but also by remanufacturing returned products. A single machine with a limited capacity in each time period is used to perform both the manufacturing and remanufacturing operations. A mathematical programming formulation is proposed to optimally solve this problem. Since the problem is NP-hard (it is a generalized version of the classical capacitated dynamic lot sizing problem), a relax-and-fix heuristic is developed to solve the problem in a reasonable amount of time. To evaluate the efficiency of the proposed algorithm, some experimental instances are generated and solved. The obtained results show the effectiveness of the proposed algorithm. © 2017

关键词： Capacitated lot sizing fix heuristic Optimization Production planning Relax Remanufacturing

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Gesture-based robot control: Design challenges and evaluation with humans

Gesture-based robot control: Design challenges and evaluatio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Enrique Coronado Jessica Villalobos Barbara Bruno Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

In this paper we introduce a gesture-based robot control framework, we discuss the adopted design principles and we report results about its evaluation with humans. Gesture-based control using wearable devices may constitute a novel form of human-robot interaction, but its implications have not been discussed in the literature. We discuss the main challenging issues, possible design guidelines and an open source, freely available implementation using commercially available devices and robots. The overall performance of the architecture, as well as its validation with 27 untrained volunteers, is reported.

关键词： Mobile robots Automobiles Robot kinematics Robot sensing systems Robot control Usability

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Editor-in-Chief

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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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Correction: Robustness of cancer microbiome signals over a broad range of methodological variation

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Oncogene 2024年第20期43卷 1579页

作者： Gregory D Sepich-Poore Daniel McDonald Evguenia Kopylova Caitlin Guccione Qiyun Zhu George Austin Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica L Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Tal Korem Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Micronoma San Diego CA USA. Feinberg School of Medicine Northwestern University Chicago IL USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Antwerp Belgium. School of Life Sciences Arizona State University Tempe AZ USA. Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Program for Mathematical Genomics Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Kraków Kraków Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Obstetrics and Gynecology Columbia University Irving Medical Center New York NY USA. Department of Bioengineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu.

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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A toolbox for dynamic and connectivity analysis of neuronal spike trains data

A toolbox for dynamic and connectivity analysis of neuronal ...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Vito Paolo Pastore Aleksandar Godjoski Sergio Martinoia Paolo Massobrio Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Genova Italy

ISBN: (纸本)9781509046041

Thanks to recent improvements in the neurotechnology, parallel recordings with an ever-increasing number of micro-transducers are now available to monitor the neuronal spiking activity of large-scale neuronal networks. At the same time, continuous improvements are required to develop computationally efficient software for processing and analyzing such huge amounts of data. In this work, we present a new tool named SPICODYN, as a possible solution to efficiently process and analyze big-data coming from in vitro multi-site recordings. By adopting the standardized HDF5 raw input data format it offers independency from the specific acquisition setup. SPICODYN allows performing pre-processing operations (spike detection), full dynamics and functional-effective connectivity analysis on the generated spike trains, and topological characterization related to the estimated connectivity.

关键词： Electrodes Entropy Biological neural networks Algorithm design and analysis Delays Tools In vitro

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Towards a new paradigm for assistive technology at home: Research challenges, design issues and performance assessment

arXiv

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arXiv 2017年

作者： Buoncompagni, Luca Bruno, Barbara Giuni, Antonella Mastrogiovanni, Fulvio Zaccaria, Renato Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy Teseo srl

Providing elderly and people with special needs, including those suffering from physical disabilities and chronic diseases, with the possibility of retaining their independence at best is one of the most important challenges our society is expected to face. Assistance models based on the home care paradigm are being adopted rapidly in almost all industrialized and emerging countries. Such paradigms hypothesize that it is necessary to ensure that the so-called Activities of Daily Living are correctly and regularly performed by the assisted person to increase the perception of an improved quality of life. This chapter describes the computational inference engine at the core of Arianna, a system able to understand whether an assisted person performs a given set of ADL and to motivate him/her in performing them through a speech-mediated motivational dialogue, using a set of nearables to be installed in an apartment, plus a wearable to be worn or fit in *** codes 68T30 Copyright © 2017, The Authors. All rights reserved.

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On the Recognition of Human Hand Touch from Robotic Skin Pressure Measurements Using Convolutional Neural Networks

On the Recognition of Human Hand Touch from Robotic Skin Pre...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： Alessandro Albini Simone Denei Giorgio Cannata Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy

This paper presents a novel approach for recognizing a human hand touch by processing pressure measurements generated by a robotic skin. Physical cooperation among humans is mainly based on the sense of touch and usually starts with hand contacts. If a robot can distinguish a human touch from a generic contact, the human-robot cooperation can be more natural and effective. The proposed approach consists in transforming the sensor pressure measurements distributed on the robot surface into a convenient 2D representation of the contact shape, i.e., a contact image. The image-based representation of contacts allows facing the problem of human touch classification by applying machine learning methods already developed for image classification. The experiments have been performed using a robotic skin, composed of 768 tactile elements, placed on a Baxter robot forearm. The contact classification has been performed using a Convolutional Neural Network obtaining an accuracy higher than 97% experimentally validating the proposed approach.

关键词： sense touch contact shape

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An Optimization Model for Polygeneration Microgrids with Renewables, Electrical and Thermal Storage: Application to the Savona Campus

An Optimization Model for Polygeneration Microgrids with Ren...

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IEEE International Conference on Environment and Electrical engineering

作者： Stefano Bracco Massimo Brignone Federico Delfino Fabio Pampararo Mansueto Rossi Giulio Ferro Michela Robba DITEN-Department of Naval Electrical Electronic and Telecommunication Engineering University of Genova Italy DIBRIS-Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

In this paper, an optimization model for polygeneration microgrids is presented. In particular, the system is characterized by trigeneration plants, renewables, district heating, thermal and electrical storage systems and flexible loads. The control variables are represented by the schedule of the power plants and of the deferrable demand, while the state variables are the energy stored in batteries and water tanks. The objective function minimizes operational costs in the day-ahead. The model is applied to the University of Genoa research infrastructures at Savona Campus, characterized by a Smart Polygeneration Microgrid and a Smart Energy Building.

关键词： Demand response polygeneration microgrids renewable energy thermal storage district heating

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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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