263 research outputs found
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network
is challenging due to the naturally unordered data structure. Among existing
works, PointNet has achieved promising results by directly learning on point
sets. However, it does not take full advantage of a point's local neighborhood
that contains fine-grained structural information which turns out to be helpful
towards better semantic learning. In this regard, we present two new operations
to improve PointNet with a more efficient exploitation of local structures. The
first one focuses on local 3D geometric structures. In analogy to a convolution
kernel for images, we define a point-set kernel as a set of learnable 3D points
that jointly respond to a set of neighboring data points according to their
geometric affinities measured by kernel correlation, adapted from a similar
technique for point cloud registration. The second one exploits local
high-dimensional feature structures by recursive feature aggregation on a
nearest-neighbor-graph computed from 3D positions. Experiments show that our
network can efficiently capture local information and robustly achieve better
performances on major datasets. Our code is available at
http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio
TSEXPLAIN: Explaining Aggregated Time Series by Surfacing Evolving Contributors
Aggregated time series are generated effortlessly everywhere, e.g., "total
confirmed covid-19 cases since 2019" and "total liquor sales over time."
Understanding "how" and "why" these key performance indicators (KPI) evolve
over time is critical to making data-informed decisions. Existing explanation
engines focus on explaining one aggregated value or the difference between two
relations. However, this falls short of explaining KPIs' continuous changes
over time. Motivated by this, we propose TSEXPLAIN, a system that explains
aggregated time series by surfacing the underlying evolving top contributors.
Under the hood, we leverage prior works on two-relations diff as a building
block and formulate a K-Segmentation problem to segment the time series such
that each segment after segmentation shares consistent explanations, i.e.,
contributors. To quantify consistency in each segment, we propose a novel
within-segment variance design that is explanation-aware; to derive the optimal
K-Segmentation scheme, we develop an efficient dynamic programming algorithm.
Experiments on synthetic and real-world datasets show that our
explanation-aware segmentation can effectively identify evolving explanations
for aggregated time series and outperform explanation-agnostic segmentation.
Further, we proposed an optimal selection strategy of K and several
optimizations to speed up TSEXPLAIN for interactive user experience, achieving
up to 13X efficiency improvement.Comment: 17 pages; Accepted by ICDE 202
Demonstration of PI2: Interactive Visualization Interface Generation for SQL Analysis in Notebook
We demonstrate PI2, the first notebook extension that can automatically
generate interactive visualization interfaces during SQL-based analyses.Comment: arXiv admin note: text overlap with arXiv:2107.0820
Historical data based energy management in a microgrid with a hybrid energy storage system
In a micro-grid, due to potential reverse output profiles of the Renewable Energy Source (RES) and the load, energy storage devices are employed to achieve high self-consumption of RES and to minimize power surplus flowing back into the main grid. This paper proposes a variable charging/discharging threshold method to manage energy storage system. And an Adaptive Intelligence Technique (AIT) is put forward to raise the power management efficiency. A battery-ultra-capacitor hybrid energy storage system (HESS) with merits of high energy and power density is used to evaluate the proposed method with onsite measured RES output data. Compared with the PSO algorithm based on the precise predicted data of the load and the RES, the results show that the proposed method can achieve better load smoothing and maximum self-consumption of the RES without the requirement of precise load and RES forecasting
Channel Parameters Identification Based on IMM Algorithm for Variant Correlation Channel
In wireless communication systems, correct knowledge of the correlation of a fading channel is essential for channel estimation. Both the reliability of the estimated channel impulse response (CIR) and the adjustment of an adaptive communication system need the accurate correlation information, which is difficult to identify especially when changing. By modeling the fading channel as a hybrid dynamic system, a channel estimation algorithm based on Interacting Multiple Model (IMM) is presented with the consideration of time-variant channel correlation. Applying the IMM algorithm, the proposed channel estimator can identify the channel correlation. With the accurate information of channel correlation, the proposed algorithm is capable of performing accurate estimation on the fading wireless channel with time-variant or time-invariant correlation. Our simulations demonstrate that the IMM based channel estimation algorithm has good performance in estimating CIR as well as in identifying the channel
correlation
Channel Parameters Identification Based on IMM Algorithm for Variant Correlation Channel
In wireless communication systems, correct knowledge of the correlation of a fading channel is essential for channel estimation. Both the reliability of the estimated channel impulse response (CIR) and the adjustment of an adaptive communication system need the accurate correlation information, which is difficult to identify especially when changing. By modeling the fading channel as a hybrid dynamic system, a channel estimation algorithm based on Interacting Multiple Model (IMM) is presented with the consideration of time-variant channel correlation. Applying the IMM algorithm, the proposed channel estimator can identify the channel correlation. With the accurate information of channel correlation, the proposed algorithm is capable of performing accurate estimation on the fading wireless channel with time-variant or time-invariant correlation. Our simulations demonstrate that the IMM based channel estimation algorithm has good performance in estimating CIR as well as in identifying the channel correlation
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