66,632 research outputs found
A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms
BowTie - A deep learning feedforward neural network for sentiment analysis
How to model and encode the semantics of human-written text and select the
type of neural network to process it are not settled issues in sentiment
analysis. Accuracy and transferability are critical issues in machine learning
in general. These properties are closely related to the loss estimates for the
trained model. I present a computationally-efficient and accurate feedforward
neural network for sentiment prediction capable of maintaining low losses. When
coupled with an effective semantics model of the text, it provides highly
accurate models with low losses. Experimental results on representative
benchmark datasets and comparisons to other methods show the advantages of the
new approach.Comment: 12 pages, 7 figures, 4 table
Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents
This paper proposes a control scheme for the quality-fair delivery of several
encoded video streams to mobile users sharing a common wireless resource. Video
quality fairness, as well as similar delivery delays are targeted among
streams. The proposed controller is implemented within some aggregator located
near the bottleneck of the network. The transmission rate among streams is
adapted based on the quality of the already encoded and buffered packets in the
aggregator. Encoding rate targets are evaluated by the aggregator and fed back
to each remote video server (fully centralized solution), or directly evaluated
by each server in a distributed way (partially distributed solution). Each
encoding rate target is adjusted for each stream independently based on the
corresponding buffer level or buffering delay in the aggregator. Communication
delays between the servers and the aggregator are taken into account. The
transmission and encoding rate control problems are studied with a
control-theoretic perspective. The system is described with a multi-input
multi-output model. Proportional Integral (PI) controllers are used to adjust
the video quality and control the aggregator buffer levels. The system
equilibrium and stability properties are studied. This provides guidelines for
choosing the parameters of the PI controllers. Experimental results show the
convergence of the proposed control system and demonstrate the improvement in
video quality fairness compared to a classical transmission rate fair streaming
solution and to a utility max-min fair approach
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