123,064 research outputs found
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
Haptic Assembly and Prototyping: An Expository Review
An important application of haptic technology to digital product development
is in virtual prototyping (VP), part of which deals with interactive planning,
simulation, and verification of assembly-related activities, collectively
called virtual assembly (VA). In spite of numerous research and development
efforts over the last two decades, the industrial adoption of haptic-assisted
VP/VA has been slower than expected. Putting hardware limitations aside, the
main roadblocks faced in software development can be traced to the lack of
effective and efficient computational models of haptic feedback. Such models
must 1) accommodate the inherent geometric complexities faced when assembling
objects of arbitrary shape; and 2) conform to the computation time limitation
imposed by the notorious frame rate requirements---namely, 1 kHz for haptic
feedback compared to the more manageable 30-60 Hz for graphic rendering. The
simultaneous fulfillment of these competing objectives is far from trivial.
This survey presents some of the conceptual and computational challenges and
opportunities as well as promising future directions in haptic-assisted VP/VA,
with a focus on haptic assembly from a geometric modeling and spatial reasoning
perspective. The main focus is on revisiting definitions and classifications of
different methods used to handle the constrained multibody simulation in
real-time, ranging from physics-based and geometry-based to hybrid and unified
approaches using a variety of auxiliary computational devices to specify,
impose, and solve assembly constraints. Particular attention is given to the
newly developed 'analytic methods' inherited from motion planning and protein
docking that have shown great promise as an alternative paradigm to the more
popular combinatorial methods.Comment: Technical Report, University of Connecticut, 201
Mathematical Software: Past, Present, and Future
This paper provides some reflections on the field of mathematical software on
the occasion of John Rice's 65th birthday. I describe some of the common themes
of research in this field and recall some significant events in its evolution.
Finally, I raise a number of issues that are of concern to future developments.Comment: To appear in the Proceedings of the International Symposium on
Computational Sciences, Purdue University, May 21-22, 1999. 20 page
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
Foreground object segmentation is a critical step for many image analysis
tasks. While automated methods can produce high-quality results, their failures
disappoint users in need of practical solutions. We propose a resource
allocation framework for predicting how best to allocate a fixed budget of
human annotation effort in order to collect higher quality segmentations for a
given batch of images and automated methods. The framework is based on a
prediction module that estimates the quality of given algorithm-drawn
segmentations. We demonstrate the value of the framework for two novel tasks
related to predicting how to distribute annotation efforts between algorithms
and humans. Specifically, we develop two systems that automatically decide, for
a batch of images, when to recruit humans versus computers to create 1) coarse
segmentations required to initialize segmentation tools and 2) final,
fine-grained segmentations. Experiments demonstrate the advantage of relying on
a mix of human and computer efforts over relying on either resource alone for
segmenting objects in images coming from three diverse modalities (visible,
phase contrast microscopy, and fluorescence microscopy)
An Information Theoretic Measure for Robot Expressivity
This paper presents a principled way to think about articulated movement for
artificial agents and a measurement of platforms that produce such movement. In
particular, in human-facing scenarios, the shape evolution of robotic platforms
will become essential in creating systems that integrate and communicate with
human counterparts. This paper provides a tool to measure the expressive
capacity or expressivity of articulated platforms. To do this, it points to the
synergistic relationship between computation and mechanization. Importantly,
this way of thinking gives an information theoretic basis for measuring and
comparing robots of increasing complexity and capability. The paper will
provide concrete examples of this measure in application to current robotic
platforms. It will also provide a comparison between the computational and
mechanical capabilities of robotic platforms and analyze order-of-magnitude
trends over the last 15 years. Implications for future work made by the paper
are to provide a method by which to quantify movement imitation, outline a way
of thinking about designing expressive robotic systems, and contextualize the
capabilities of current robotic systems.Comment: Rejected from RSS 201
Framework for Version Control & Dependency Link of Components & Products in a Software Product Line
Software product line deals with the assembly of products from existing core
assets commonly known as components and continuous growth in the core assets as
we proceed with production. This idea has emerged as vital in terms of software
development from component-based architecture. Since in software product line
one has to deal with number of products and components simultaneous therefore
there is a need to develop a strategy, which will help to store components and
products information in such a way that they can be traced easily for further
development. This storage strategy should reflect a relationship between
products and components so that product history with reference to components
can be traced and vise versa. In this paper we have presented a tree structure
based storage strategy for components and products in software product line.
This strategy will enable us to store the vital information about components
and products with a relationship of their composition and utilization. We
implemented this concept and simulated the software product line environment
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
This paper reviews recent advances in the field of optimization under
uncertainty via a modern data lens, highlights key research challenges and
promise of data-driven optimization that organically integrates machine
learning and mathematical programming for decision-making under uncertainty,
and identifies potential research opportunities. A brief review of classical
mathematical programming techniques for hedging against uncertainty is first
presented, along with their wide spectrum of applications in Process Systems
Engineering. A comprehensive review and classification of the relevant
publications on data-driven distributionally robust optimization, data-driven
chance constrained program, data-driven robust optimization, and data-driven
scenario-based optimization is then presented. This paper also identifies
fertile avenues for future research that focuses on a closed-loop data-driven
optimization framework, which allows the feedback from mathematical programming
to machine learning, as well as scenario-based optimization leveraging the
power of deep learning techniques. Perspectives on online learning-based
data-driven multistage optimization with a learning-while-optimizing scheme is
presented
Logic BIST: State-of-the-Art and Open Problems
Many believe that in-field hardware faults are too rare in practice to
justify the need for Logic Built-In Self-Test (LBIST) in a design. Until now,
LBIST was primarily used in safety-critical applications. However, this may
change soon. First, even if costly methods like burn-in are applied, it is no
longer possible to get rid of all latent defects in devices at leading-edge
technology. Second, demands for high reliability spread to consumer electronics
as smartphones replace our wallets and IDs. However, today many ASIC vendors
are reluctant to use LBIST. In this paper, we describe the needs for successful
deployment of LBIST in the industrial practice and discuss how these needs can
be addressed. Our work is hoped to attract a wider attention to this important
research topic.Comment: 6 pages, 3 figure
In-Band Full-Duplex Wireless: Challenges and Opportunities
In-band full-duplex (IBFD) operation has emerged as an attractive solution
for increasing the throughput of wireless communication systems and networks.
With IBFD, a wireless terminal is allowed to transmit and receive
simultaneously in the same frequency band. This tutorial paper reviews the main
concepts of IBFD wireless. Because one the biggest practical impediments to
IBFD operation is the presence of self-interference, i.e., the interference
caused by an IBFD node's own transmissions to its desired receptions, this
tutorial surveys a wide range of IBFD self-interference mitigation techniques.
Also discussed are numerous other research challenges and opportunities in the
design and analysis of IBFD wireless systems
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers.Comment: 36 pages, 1 figure. arXiv admin note: substantial text overlap with
arXiv:1404.782
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