509,543 research outputs found
A Spectrophotometric Method to Determine the Inclination of Class I Objects
A new method which enables us to estimate the inclination of Class I young
stellar objects is proposed. Since Class I objects are not spherically
symmetric, it is likely that the observed feature is sensitive to the
inclination of the system. Thus, we construct a protostar model by carefully
treating two-dimensional (2D) radiative transfer and radiative equilibrium. We
show from the present 2D numerical simulations that the emergent luminosity
L_SED,which is the frequency integration of spectral energy distribution (SED),
depends strongly on the inclination of the system i, whereas the peak flux is
insensitive to i. Based on this result, we introduce a novel indicator f_L,
which is the ratio of L_SED to the peak flux, as a good measure for the
inclination. By using f_L, we can determine the inclination regardless of the
other physical parameters. The inclination would be determined by f_L within
the accuracy of +- 5 degree, if the opening angle of bipolar outflows is
specified by any other procedure. Since this spectrophotometric method is
easier than a geometrical method or a full SED fitting method, this method
could be a powerful tool to investigate the feature of protostars statistically
with observational data which will be provided by future missions, such as
SIRTF, ASTRO-F, and ALMA.Comment: 14 pages, 9 figures, accepted by Ap
SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition
This paper presents a generic approach for applying the cognitive workload
recognizer by exploiting common electroencephalogram (EEG) patterns across
different human-machine tasks and individual sets. We propose a neural network
called SCVCNet, which eliminates task- and individual-set-related interferences
in EEGs by analyzing finer-grained frequency structures in the power spectral
densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC)
operation, where paired input layers representing the theta and alpha power are
employed. By extracting the weights from a kernel matrix's central row and
column, we compute the weighted sum of the two vectors around a specified scalp
location. Next, we introduce an inter-frequency-point feature integration
module to fuse the SCVC feature maps. Finally, we combined the two modules with
the output-channel pooling and classification layers to construct the model. To
train the SCVCNet, we employ the regularized least-square method with ridge
regression and the extreme learning machine theory. We validate its performance
using three databases, each consisting of distinct tasks performed by
independent participant groups. The average accuracy (0.6813 and 0.6229) and F1
score (0.6743 and 0.6076) achieved in two different validation paradigms show
partially higher performance than the previous works. All features and
algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.Comment: 12 page
Non-intrusive load monitoring based on low frequency active power measurements
A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac-
tive power signal is presented. This method works e
ectively with a single active power measurement
taken at a low sampling rate (1 s). The proposed method utilizes the
Karhunen Lo
́
eve
(KL) expan-
sion to decompose windows of active power signals into subspace components in order to construct a
unique set of features, referred to as signatures, from individual and aggregated active power signals.
Similar signal windows were clustered in to one group prior to feature extraction. The clustering was
performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal
windows and power levels of subspace components were utilized to reduce the number of possible ap-
pliance combinations and their energy level combinations. Then, the turned on appliance combination
and the energy contribution from individual appliances were determined through the Maximum a Pos-
teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the
usage patterns of appliances at each residence. The proposed NILM method was validated using data
from two public databases:
tracebase
and reference energy disaggregation data set (REDD). The pre-
sented results demonstrate the ability of the proposed method to accurately identify and disaggregate
individual energy contributions of turned on appliance combinations in real households. Furthermore,
the results emphasise the importance of clustering and the integration of the usage behaviour pattern in
the proposed NILM method for real household
Support Vector Machines in High Energy Physics
This lecture will introduce the Support Vector algorithms for classification
and regression. They are an application of the so called kernel trick, which
allows the extension of a certain class of linear algorithms to the non linear
case. The kernel trick will be introduced and in the context of structural risk
minimization, large margin algorithms for classification and regression will be
presented. Current applications in high energy physics will be discussed.Comment: 11 pages, 12 figures. Part of the proceedings of the Track
'Computational Intelligence for HEP Data Analysis' at iCSC 200
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