33,811 research outputs found
Probabilistic Sparse Subspace Clustering Using Delayed Association
Discovering and clustering subspaces in high-dimensional data is a
fundamental problem of machine learning with a wide range of applications in
data mining, computer vision, and pattern recognition. Earlier methods divided
the problem into two separate stages of finding the similarity matrix and
finding clusters. Similar to some recent works, we integrate these two steps
using a joint optimization approach. We make the following contributions: (i)
we estimate the reliability of the cluster assignment for each point before
assigning a point to a subspace. We group the data points into two groups of
"certain" and "uncertain", with the assignment of latter group delayed until
their subspace association certainty improves. (ii) We demonstrate that delayed
association is better suited for clustering subspaces that have ambiguities,
i.e. when subspaces intersect or data are contaminated with outliers/noise.
(iii) We demonstrate experimentally that such delayed probabilistic association
leads to a more accurate self-representation and final clusters. The proposed
method has higher accuracy both for points that exclusively lie in one
subspace, and those that are on the intersection of subspaces. (iv) We show
that delayed association leads to huge reduction of computational cost, since
it allows for incremental spectral clustering
Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries
Accurate on-board capacity estimation is of critical importance in
lithium-ion battery applications. Battery charging/discharging often occurs
under a constant current load, and hence voltage vs. time measurements under
this condition may be accessible in practice. This paper presents a data-driven
diagnostic technique, Gaussian Process regression for In-situ Capacity
Estimation (GP-ICE), which estimates battery capacity using voltage
measurements over short periods of galvanostatic operation. Unlike previous
works, GP-ICE does not rely on interpreting the voltage-time data as
Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes
the need to differentiate the voltage-time data (a process which amplifies
measurement noise), and the requirement that the range of voltage measurements
encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets,
consisting of 8 and 20 cells respectively. In each case, within certain voltage
ranges, as little as 10 seconds of galvanostatic operation enables capacity
estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial
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