4,507 research outputs found
Recommended from our members
Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme
The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others
DoR Communicator - February 2014
https://digitalcommons.fiu.edu/research_newsletter/1005/thumbnail.jp
Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems
WiGig networks and 60 GHz frequency communications have a lot of potential
for commercial and personal use. They can offer extremely high transmission
rates but at the cost of low range and penetration. Due to these issues, WiGig
systems are unstable and need to rely on frequent handovers to maintain
high-quality connections. However, this solution is problematic as it forces
users into bad connections and downtime before they are switched to a better
access point. In this work, we use Machine Learning to identify patterns in
user behaviors and predict user actions. This prediction is used to do
proactive handovers, switching users to access points with better future
transmission rates and a more stable environment based on the future state of
the user. Results show that not only the proposal is effective at predicting
channel data, but the use of such predictions improves system performance and
avoids unnecessary handovers.Comment: Submitted to IEEE Networ
- …