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 γ\gamma photons with extremely large orbital angular momenta

Vortex $\gamma$ 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 $\gamma$ photons. The multiphoton absorption of electrons enables the vortex $\gamma$ 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 $\gamma$ photons to carry adjustable OAM quantum numbers from tens to thousands of units, even at moderate laser intensities. $\gamma$ 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