4,602 research outputs found
Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data
Activity recognition from sensor data deals with various challenges, such as
overlapping activities, activity labeling, and activity detection. Although
each challenge in the field of recognition has great importance, the most
important one refers to online activity recognition. The present study tries to
use online hierarchical hidden Markov model to detect an activity on the stream
of sensor data which can predict the activity in the environment with any
sensor event. The activity recognition samples were labeled by the statistical
features such as the duration of activity. The results of our proposed method
test on two different datasets of smart homes in the real world showed that one
dataset has improved 4% and reached (59%) while the results reached 64.6% for
the other data by using the best methods
Optimal Local and Remote Controllers with Unreliable Communication
We consider a decentralized optimal control problem for a linear plant
controlled by two controllers, a local controller and a remote controller. The
local controller directly observes the state of the plant and can inform the
remote controller of the plant state through a packet-drop channel. We assume
that the remote controller is able to send acknowledgments to the local
controller to signal the successful receipt of transmitted packets. The
objective of the two controllers is to cooperatively minimize a quadratic
performance cost. We provide a dynamic program for this decentralized control
problem using the common information approach. Although our problem is not a
partially nested LQG problem, we obtain explicit optimal strategies for the two
controllers. In the optimal strategies, both controllers compute a common
estimate of the plant state based on the common information. The remote
controller's action is linear in the common estimated state, and the local
controller's action is linear in both the actual state and the common estimated
state
Model Predictive BESS Control for Demand Charge Management and PV-Utilization Improvement
Adoption of battery energy storage systems for behind-the-meters application
offers valuable benefits for demand charge management as well as increasing
PV-utilization. The key point is that while the benefit/cost ratio for a single
application may not be favorable for economic benefits of storage systems,
stacked services can provide multiple revenue streams for the same investment.
Under this framework, we propose a model predictive controller to reduce demand
charge cost and enhance PV-utilization level simultaneously. Different load
patterns have been considered in this study and results are compared to the
conventional rule-based controller. The results verified that the proposed
controller provides satisfactory performance by improving the PV-utilization
rate between 60% to 80% without significant changes in demand charge (DC)
saving. Furthermore, our results suggest that batteries can be used for
stacking multiple services to improve their benefits. Quantitative analysis for
PV-utilization as a function of battery size and prediction time window has
also been carried out.Comment: Accepted in: Conference on Innovative Smart Grid Technology (ISGT),
Washington, DC, 201
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