181,140 research outputs found
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by to
and energy consumption by to compared with the
state-of-the-art compression algorithms.Comment: Accepted by SenSys '1
Fast training of self organizing maps for the visual exploration of molecular compounds
Visual exploration of scientific data in life science
area is a growing research field due to the large amount of
available data. The Kohonen’s Self Organizing Map (SOM) is
a widely used tool for visualization of multidimensional data.
In this paper we present a fast learning algorithm for SOMs
that uses a simulated annealing method to adapt the learning
parameters. The algorithm has been adopted in a data analysis
framework for the generation of similarity maps. Such maps
provide an effective tool for the visual exploration of large and
multi-dimensional input spaces. The approach has been applied
to data generated during the High Throughput Screening
of molecular compounds; the generated maps allow a visual
exploration of molecules with similar topological properties.
The experimental analysis on real world data from the
National Cancer Institute shows the speed up of the proposed
SOM training process in comparison to a traditional approach.
The resulting visual landscape groups molecules with similar
chemical properties in densely connected regions
Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition
This paper proposes a novel latent semantic learning method for extracting
high-level features (i.e. latent semantics) from a large vocabulary of abundant
mid-level features (i.e. visual keywords) with structured sparse
representation, which can help to bridge the semantic gap in the challenging
task of human action recognition. To discover the manifold structure of
midlevel features, we develop a spectral embedding approach to latent semantic
learning based on L1-graph, without the need to tune any parameter for graph
construction as a key step of manifold learning. More importantly, we construct
the L1-graph with structured sparse representation, which can be obtained by
structured sparse coding with its structured sparsity ensured by novel L1-norm
hypergraph regularization over mid-level features. In the new embedding space,
we learn latent semantics automatically from abundant mid-level features
through spectral clustering. The learnt latent semantics can be readily used
for human action recognition with SVM by defining a histogram intersection
kernel. Different from the traditional latent semantic analysis based on topic
models, our latent semantic learning method can explore the manifold structure
of mid-level features in both L1-graph construction and spectral embedding,
which results in compact but discriminative high-level features. The
experimental results on the commonly used KTH action dataset and unconstrained
YouTube action dataset show the superior performance of our method.Comment: The short version of this paper appears in ICCV 201
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