1,002,364 research outputs found
Computer Architectures to Close the Loop in Real-time Optimization
© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy
A configurable platform for synthetic chemistry incorporating an in-line benchtop NMR that is capable of monitoring and controlling organic reactions in real-time is presented. The platform is controlled via a modular LabView software control system for the hardware, NMR, data analysis and feedback optimization. Using this platform we report the real-time advanced structural characterization of reaction mixtures, including 19F, 13C, DEPT, 2D NMR spectroscopy (COSY, HSQC and 19F-COSY) for the first time. Finally, the potential of this technique is demonstrated through the optimization of a catalytic organic reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereoselectivity, multi-nuclear measurements or 2D correlations
An investigation of optimization techniques for drawing computer graphics displays
Techniques for reducing vector data plotting time are studied. The choice of tolerances in optimization and the application of optimization to plots produced on real time interactive display devices are discussed. All results are developed relative to plotting packages and support hardware so that results are useful in real world situations
Credible Autocoding of Convex Optimization Algorithms
The efficiency of modern optimization methods, coupled with increasing
computational resources, has led to the possibility of real-time optimization
algorithms acting in safety critical roles. There is a considerable body of
mathematical proofs on on-line optimization programs which can be leveraged to
assist in the development and verification of their implementation. In this
paper, we demonstrate how theoretical proofs of real-time optimization
algorithms can be used to describe functional properties at the level of the
code, thereby making it accessible for the formal methods community. The
running example used in this paper is a generic semi-definite programming (SDP)
solver. Semi-definite programs can encode a wide variety of optimization
problems and can be solved in polynomial time at a given accuracy. We describe
a top-to-down approach that transforms a high-level analysis of the algorithm
into useful code annotations. We formulate some general remarks about how such
a task can be incorporated into a convex programming autocoder. We then take a
first step towards the automatic verification of the optimization program by
identifying key issues to be adressed in future work
IPv6 mobility support for real-time multimedia communications: A survey
Mobile Internet protocol version 6(MIPv6) route optimization improves triangular routing problem that exists in MIPv4 environment.Route optimization of Session Initiation Protocol (SIP) over MIPv6 provides ef�cient real-time multimedia applications to users. This article provides a survey of SIP over MIPv6. We review the processes involved during the setting up of a SIP call and during mid-call SIP mobility. When SIP transmits real-time multimedia applications in a wireless environment, the mobile node (MN) may move from one access router (AR) to
another AR, handing over control from one AR to the other. High handover latency degrades the quality of real-time multimedia applications due to the fact that real-time multimedia applications are delay-sensitive.Handover latency is an important issue to discuss.Reduction of handover latency can be made possible with the use of SIP's hierarchical registration. On the other hand, hybrid hierarchical and fast handover SIP's registration performs better compared to hierarchical registration. Finally, we present the directions for future research
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