745 research outputs found
Semi-supervised Learning with Deterministic Labeling and Large Margin Projection
The centrality and diversity of the labeled data are very influential to the
performance of semi-supervised learning (SSL), but most SSL models select the
labeled data randomly. This study first construct a leading forest that forms a
partially ordered topological space in an unsupervised way, and select a group
of most representative samples to label with one shot (differs from active
learning essentially) using property of homeomorphism. Then a kernelized large
margin metric is efficiently learned for the selected data to classify the
remaining unlabeled sample. Optimal leading forest (OLF) has been observed to
have the advantage of revealing the difference evolution along a path within a
subtree. Therefore, we formulate an optimization problem based on OLF to select
the samples. Also with OLF, the multiple local metrics learning is facilitated
to address multi-modal and mix-modal problem in SSL, especially when the number
of class is large. Attribute to this novel design, stableness and accuracy of
the performance is significantly improved when compared with the
state-of-the-art graph SSL methods. The extensive experimental studies have
shown that the proposed method achieved encouraging accuracy and efficiency.
Code has been made available at https://github.com/alanxuji/DeLaLA.Comment: 12 pages, ready to submit to a journa
Feasibility of an innovative amorphous silicon photovoltaic/thermal system for medium temperature applications
Medium temperature photovoltaic/thermal (PV/T) systems have immense potential in the applications of absorption cooling, thermoelectric generation, and organic Rankine cycle power generation, etc. Amorphous silicon (a-Si) cells are promising in such applications regarding the low temperature coefficient, thermal annealing effect, thin film and avoidance of large thermal stress and breakdown at fluctuating temperatures. However, experimental study on the a-Si PV/T system is rarely reported. So far the feasibility of medium temperature PV/T systems using a-Si cells has not been demonstrated. In this study, the design and construction of an innovative a-Si PV/T system of stainless steel substrate are presented. Long-term outdoor performance of the system operating at medium temperature has been monitored in the past 15 months. The average electrical efficiency was 5.65%, 5.41% and 5.30% at the initial, intermediate and final phases of the long-test test, accompanied with a daily average thermal efficiency from about 21% to 31% in the non-heating season. The thermal and electrical performance of the system at 60 °C, 70 °C and 80 °C are also analyzed and compared. Moreover, a distributed parameter model with experimental validation is developed for an inside view of the heat transfer and power generation and to predict the system performance in various conditions. Technically, medium temperature operation has not resulted in interruption or observable deformation of the a-Si PV/T system during the period. The technical and thermodynamic feasibility of the a-Si PV/T system at medium operating temperature is demonstrated by the experimental and simulation results
Detection of Outliers in a Time Series of Available Parking Spaces
With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively
Attentive multi-scale aggregation based action recognition and its application in power substation operation training
With the rapid development of the power system and increasing demand for intelligence, substation operation training has received more attention. Action recognition is a monitoring and analysis system based on computer vision and artificial intelligence technology that can automatically identify and track personnel actions in video frames. The system accurately identifies abnormal behaviors such as illegal operations and provides real-time feedback to trainers or surveillance systems. The commonly adopted strategy for action recognition is to first extract human skeletons from videos and then recognize the skeleton sequences. Although graph convolutional networks (GCN)-based skeleton-based recognition methods have achieved impressive performance, they operate in spatial dimensions and cannot accurately describe the dependence between different time intervals in the temporal dimension. Additionally, existing methods typically handle the temporal and spatial dimensions separately, lacking effective communication between them. To address these issues, we propose a skeleton-based method that aggregates convolutional information of different scales in the time dimension to form a new scale dimension. We also introduce a space-time-scale attention module that enables effective communication and weight generation between the three dimensions for prediction. Our proposed method is validated on public datasets NTU60 and NTU120, with experimental results verifying its effectiveness. For substation operation training, we built a real-time recognition system based on our proposed method. We collected over 400 videos for evaluation, including 5 categories of actions, and achieved an accuracy of over 98%
Generation of photons with extremely large orbital angular momenta
Vortex photons, which carry large intrinsic orbital angular momenta
(OAM), have significant applications in nuclear, atomic, hadron, particle and
astro-physics, but their production remains unclear. In this work, we
investigate the generation of such photons from nonlinear Compton scattering of
circularly polarized monochromatic lasers on vortex electrons. We develop a
quantum radiation theory for ultrarelativistic vortex electrons in lasers by
using the harmonics expansion and spin eigenfunctions, which allows us to
explore the kinematical characteristics, angular momentum transfer mechanisms,
and formation conditions of vortex photons. The multiphoton absorption
of electrons enables the vortex photons, with fixed polarizations and
energies, to exist in mixed states comprised of multiple harmonics. Each
harmonic represents a vortex eigenmode and has transverse momentum broadening
due to transverse momenta of the vortex electrons. The large topological
charges associated with vortex electrons offer the possibility for
photons to carry adjustable OAM quantum numbers from tens to thousands of
units, even at moderate laser intensities. photons with large OAM and
transverse coherence length can assist in influencing quantum selection rules
and extracting phase of the scattering amplitude in scattering processes.Comment: 7 pages, 4 figure
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