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Stochastic Dynamical Modeling of Turbulent Flows

control, Robotics, and Autonomous Systems 1000年第1期3卷 195-219页

作者： A. Zare T.T. Georgiou M.R. Jovanović 1Department of Mechanical Engineering University of Texas at Dallas Richardson Texas 75080 USA 2Department of Mechanical and Aerospace Engineering University of California Irvine California 92697 USA 3Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA email: mihailo@usc.edu

Advanced measurement techniques and high-performance computing have made large data sets available for a range of turbulent flows in engineering applications. Drawing on this abundance of data, dynamical models that reproduce structural and statistical features of turbulent flows enable effective model-based flow control strategies. This review describes a framework for completing second-order statistics of turbulent flows using models based on the Navier–Stokes equations linearized around the turbulent mean velocity. Dynamical couplings between states of the linearized model dictate structural constraints on the statistics of flow fluctuations. Colored-in-time stochastic forcing that drives the linearized model is then sought to account for and reconcile dynamics with available data (that is, partially known statistics). The number of dynamical degrees of freedom that are directly affected by stochastic excitation is minimized as a measure of model parsimony. The spectral content of the resulting colored-in-time stochastic contribution can alternatively arise from a low-rank structural perturbation of the linearized dynamical generator, pointing to suitable dynamical corrections that may account for the absence of the nonlinear interactions in the linearized model.

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Robotic Micromanipulation: Fundamentals and Applications

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control, Robotics, and Autonomous Systems 1000年第1期2卷 181-203页

作者： Zhuoran Zhang Xian Wang Jun Liu Changsheng Dai Yu Sun 1Department of Mechanical and Industrial Engineering University of Toronto Toronto Ontario M5S 3G8 Canada email: zhuoran@mie.utoronto.ca xianjeremy.wang@mail.utoronto.ca ljun.liu@mail.utoronto.ca changsheng.dai@mail.utoronto.ca sun@mie.utoronto.ca 2Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto Ontario M5S 3G8 Canada 3Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G8 Canada

Robotic micromanipulation is a relatively young field. However, after three decades of development and evolution, the fundamental physics; techniques for sensing, actuation, and control; tool sets and systems; and, more importantly, a research community are now in place. This article reviews the fundamentals of robotic micromanipulation, including how micromanipulators and end effectors are actuated and controlled, how remote physical fields are utilized for micromanipulation, how visual servoing is implemented under an optical microscope, how force is sensed and controlled at the micro- and nanonewton levels, and the similarities and differences between robotic manipulation at the micro- and macroscales. We also review representative milestones over the past three decades and discuss potential future trends of this field.

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The Visual Cortex in Context

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Vision Science 1000年第1期5卷 317-339页

作者： Emmanouil Froudarakis Paul G. Fahey Jacob Reimer Stelios M. Smirnakis Edward J. Tehovnik Andreas S. Tolias 2Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas 77030 USA 1Department of Neuroscience Baylor College of Medicine Houston Texas 77030 USA email: astolias@bcm.edu 4Jamaica Plain VA Medical Center Boston Massachusetts 02130 USA 3Department of Neurology Brigham and Women's Hospital Boston Massachusetts 02115 USA 5Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

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Modeling and control of Discrete-event Dynamic Systems 1

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丛书名： Advanced Textbooks in control and Signal Processing

1000年

作者： Branislav Hrúz MengChu Zhou

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Enabling Cost-effective Wireless Data Collection by Piggybacking on Delivery Drones in Agriculture

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ACM Transactions on Sensor Networks 1000年

作者： Chaocan Xiang Wenhui Cheng Xiao Zheng Tao Wu Xiaochen Fan Yingjie Wang Yanlin Zhou Fu Xiao College of Computer Science Chongqing University China 1) Anhui University of Technology China 2) Institute of Artificial Intelligence Hefei Comprehensive National Science Center China National University of Defense Technology China 1) Institute for Electronics and Information Technology in Tianjin Tsinghua University China 2) Department of Electronic Engineering Tsinghua University China 3) Consulting Center for Strategic Assessment Academy of Military Science China School of Computer and Control Engineering Yantai University China School of Computer Nanjing University of Posts and Telecommunications China

Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated drones via deployment and maintenance, which is insufficient regarding resource and cost-efficiency. In contrast, leveraging existing delivery drones for the data collection on their way of delivery, called delivery drones’ piggybacking, is a promising solution. For achieving such cost-efficiency, drone scheduling inevitably stands in front, but the delivery missions involved have escalated it to a wholly different and unexplored problem. As an attempt, we first survey 514 delivery workers and conduct field experiments; noticeably, the collection cost, which mostly comes from the energy consumption of drones’ piggybacking, is determined by the decisions on package-route scheduling and data collection time distribution. Based on such findings, we build a new model that jointly optimizes these two decisions to maximize data collection amount, subject to the collection budget and delivery constraints. Further model analysis finds it a Mixed Integer Non-Linear Programming problem, which is NP-hard. The major challenge stems from interdependence entangling the two decisions. For this point, we propose Delta, a \(\frac{1}{9+\delta }\)-approximation delivery drone scheduling algorithm. The key idea is to devise an approximate collection time distribution scheme leveraging energy slicing, which transforms the complex problem with two interdependent variables into a submodular function maximization problem only with one variable. The theoretical proofs and extensive evaluations verify the effectiveness and the near-optimal performance of Delta.

关键词： Wireless Data Collection Drone’s Piggybacking Energy Consumption

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The Role of Physics-Based Simulators in Robotics

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control, Robotics, and Autonomous Systems 1000年第1期4卷 35-58页

作者： C. Karen Liu Dan Negrut 1Department of Computer Science Stanford University Stanford California 94305 USA email: karenliu@cs.stanford.edu 2Department of Mechanical Engineering University of Wisconsin–Madison Madison Wisconsin 53706 USA email: negrut@wisc.edu

Physics-based simulation provides an accelerated and safe avenue for developing, verifying, and testing robotic control algorithms and prototype designs. In the quest to leverage machine learning for developing AI-enabled robots, physics-based simulation can generate a wealth of labeled training data in a short amount of time. Physics-based simulation also creates an ideal proving ground for developing intelligent robots that can both learn from their mistakes and be verifiable. This article provides an overview of the use of simulation in robotics, emphasizing how robots (with sensing and actuation components), the environment they operate in, and the humans they interact with are simulated in practice. It concludes with an overview of existing tools for simulation in robotics and a short discussion of aspects that limit the role that simulation plays today in intelligent robot design.

关键词： robot simulation testing through simulation virtual prototyping of robots

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The Microbiome and Human Biology

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Genomics and Human Genetics 1000年第1期18卷 65-86页

作者： Rob Knight Chris Callewaert Clarisse Marotz Embriette R. Hyde Justine W. Debelius Daniel McDonald Mitchell L. Sogin 3Center for Microbiome Innovation University of California San Diego La Jolla California 92093 2Department of Computer Science and Engineering University of California San Diego La Jolla California 92093 1Department of Pediatrics University of California San Diego La Jolla California 92093 email: robknight@ucsd.edu 4Center for Microbial Ecology and Technology Ghent University 9000 Ghent Belgium 5Josephine Bay Paul Center Marine Biological Laboratory Woods Hole Massachusetts 02543

Over the past few years, microbiome research has dramatically reshaped our understanding of human biology. New insights range from an enhanced understanding of how microbes mediate digestion and disease processes (e.g., in inflammatory bowel disease) to surprising associations with Parkinson's disease, autism, and depression. In this review, we describe how new generations of sequencing technology, analytical advances coupled to new software capabilities, and the integration of animal model data have led to these new discoveries. We also discuss the prospects for integrating studies of the microbiome, metabolome, and immune system, with the goal of elucidating mechanisms that govern their interactions. This systems-level understanding will change how we think about ourselves as organisms.

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Game Theory and control

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control, Robotics, and Autonomous Systems 1000年第1期1卷 105-134页

作者： Jason R. Marden Jeff S. Shamma 1Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA email: jrmarden@ece.ucsb.edu 2Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia email: jeff.shamma@kaust.edu.sa

Game theory is the study of decision problems in which there are multiple decision makers and the quality of a decision maker's choice depends on both that choice and the choices of others. While game theory has been studied predominantly as a modeling paradigm in the mathematical social sciences, there is a strong connection to control systems in that a controller can be viewed as a decision-making entity. Accordingly, game theory is relevant in settings with multiple interacting controllers. This article presents an introduction to game theory, followed by a sampling of results in three specific control theory topics where game theory has played a significant role: (a) zero-sum games, in which the two competing players are a controller and an adversarial environment; (b) team games, in which several controllers pursue a common goal but have access to different information; and (c) distributed control, in which both a game and online adaptive rules are designed to enable distributed interacting subsystems to achieve a collective objective.

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Internal Models in control, Bioengineering, and Neuroscience

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control, Robotics, and Autonomous Systems 1000年第1期5卷 55-79页

作者： Michelangelo Bin Jie Huang Alberto Isidori Lorenzo Marconi Matteo Mischiati Eduardo Sontag 1Department of Electrical and Electronic Engineering Imperial College London London United Kingdom email: m.bin@imperial.ac.uk 2Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong China email: jhuang@mae.cuhk.edu.hk 3Department of Computer Control and Management Engineering Sapienza Università di Roma Rome Italy email: albisidori@dis.uniroma1.it 4Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy email: lorenzo.marconi@unibo.it 5Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA email: mischiatim@*** 6Department of Electrical and Computer Engineering and Department of Bioengineering Northeastern University Boston Massachusetts USA email: sontag@***

Internal models are nowadays customarily used in different domains of science and engineering to describe how living organisms or artificial computational units embed their acquired knowledge about recurring events taking place in the surrounding environment. This article reviews the internal model principle in control theory, bioengineering, and neuroscience, illustrating the fundamental concepts and theoretical developments of the few last decades of research.

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Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers

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Biomedical Data Science 1000年第1期7卷 107-129页

作者： James A. Cahill Leslie A. Smith Soumya Gottipati Tina Salehi Torabi Kiley Graim 1Department of Environmental Engineering Sciences University of Florida Gainesville Florida USA 2University of Florida Genetics Institute University of Florida Gainesville Florida USA email: kgraim@ufl.edu 3Department of Computer and Information Science and Engineering University of Florida Gainesville Florida USA 4Department of Computer Science Princeton University Princeton New Jersey USA 5University of Florida Health Cancer Center University of Florida Gainesville Florida USA

Dogs are humanity's oldest friend, the first species we domesticated 20,000–40,000 years ago. In this unequaled collaboration, dogs have inadvertently but serendipitously been molded into a potent human cancer model. Unlike many common model species, dogs are raised in the same environment as humans and present with spontaneous tumors with human-like comorbidities, immunocompetency, and heterogeneity. In breast, bladder, blood, and several pediatric cancers, in-depth profiling of dog and human tumors has established the benefits of the dog model. In addition to this clinical and molecular similarity, veterinary studies indicate that domestic dogs have relatively high tumor incidence rates. As a result, there are a plethora of data for analysis, the statistical power of which is bolstered by substantial breed-specific variability. As such, dog tumors provide a unique opportunity to interrogate the molecular factors underpinning cancer and facilitate the modeling of new therapeutic targets. This review discusses the emerging field of comparative oncology, how it complements human and rodent cancer studies, and where challenges remain, given the rapid proliferation of genomic resources. Increasingly, it appears that human's best friend is becoming an irreplaceable component of oncology research.

关键词： comparative genomics cancer immunotherapy alternative model organisms dog

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