435 research outputs found
A Rapid Monitoring Method for Natural Gas Safety Monitoring
The quick leakage alarm and the accurate concentration prediction are two important aspects of natural gas safety monitoring.  In this paper, a rapid monitoring method of sensor data sharing, rapid leakage alarm and simultaneous output of concentrations prediction is proposed to accelerate the alarm speed and predict the possible impact of leakage.  In this method, the Dempster-Shafer evidence theory is used to fuse the trend judgment and the CUSUM (cumulative sum) and the Gauss-Newton iteration is used to predict the concentration. The experiment system based on the TGS2611 natural gas sensor was built.  The results show that the fusion method is significantly better than the single monitoring method.  The alarm time of fusion method was more advanced than that of the CUSUM method and the trend method (being averagely, 10.4% and 7.6% in advance in the CUSUM method and the trend method respectively).  The relative deviations of the predicted concentration were the maximum (13.3%) at 2000 ppm (parts per million) and the minimum (0.8%) at 6000 ppm, respectively
A Rapid Monitoring Method for Natural Gas Safety Monitoring
The quick leakage alarm and the accurate concentration prediction are two important aspects of natural gas safety monitoring.  In this paper, a rapid monitoring method of sensor data sharing, rapid leakage alarm and simultaneous output of concentrations prediction is proposed to accelerate the alarm speed and predict the possible impact of leakage.  In this method, the Dempster-Shafer evidence theory is used to fuse the trend judgment and the CUSUM (cumulative sum) and the Gauss-Newton iteration is used to predict the concentration. The experiment system based on the TGS2611 natural gas sensor was built.  The results show that the fusion method is significantly better than the single monitoring method.  The alarm time of fusion method was more advanced than that of the CUSUM method and the trend method (being averagely, 10.4% and 7.6% in advance in the CUSUM method and the trend method respectively).  The relative deviations of the predicted concentration were the maximum (13.3%) at 2000 ppm (parts per million) and the minimum (0.8%) at 6000 ppm, respectively
Prompt-Based Zero- and Few-Shot Node Classification: A Multimodal Approach
Multimodal data empowers machine learning models to better understand the
world from various perspectives. In this work, we study the combination of
\emph{text and graph} modalities, a challenging but understudied combination
which is prevalent across multiple settings including citation networks, social
media, and the web. We focus on the popular task of node classification using
limited labels; in particular, under the zero- and few-shot scenarios. In
contrast to the standard pipeline which feeds standard precomputed (e.g.,
bag-of-words) text features into a graph neural network, we propose
\textbf{T}ext-\textbf{A}nd-\textbf{G}raph (TAG) learning, a more deeply
multimodal approach that integrates the raw texts and graph topology into the
model design, and can effectively learn from limited supervised signals without
any meta-learning procedure. TAG is a two-stage model with (1) a prompt- and
graph-based module which generates prior logits that can be directly used for
zero-shot node classification, and (2) a trainable module that further
calibrates these prior logits in a few-shot manner. Experiments on two node
classification datasets show that TAG outperforms all the baselines by a large
margin in both zero- and few-shot settings.Comment: Work in progres
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Training task-completion dialogue agents with reinforcement learning usually
requires a large number of real user experiences. The Dyna-Q algorithm extends
Q-learning by integrating a world model, and thus can effectively boost
training efficiency using simulated experiences generated by the world model.
The effectiveness of Dyna-Q, however, depends on the quality of the world model
- or implicitly, the pre-specified ratio of real vs. simulated experiences used
for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ)
framework by integrating a switcher that automatically determines whether to
use a real or simulated experience for Q-learning. Furthermore, we explore the
use of active learning for improving sample efficiency, by encouraging the
world model to generate simulated experiences in the state-action space where
the agent has not (fully) explored. Our results show that by combining switcher
and active learning, the new framework named as Switch-based Active Deep Dyna-Q
(Switch-DDQ), leads to significant improvement over DDQ and Q-learning
baselines in both simulation and human evaluations.Comment: 8 pages, 9 figures, AAAI 201
Polynomial based key predistribution scheme in wireless mesh networks
Wireless mesh networks (WMNs) have the ability to integrate with other networks while providing a fast and cost-saving deployment. The network security is one of important challenge problems in this kind of networks. This paper is focused on key management between mesh and sensor networks. We propose an efficient key pre-distribution scheme based on two polynomials in wireless mesh networks by employing the nature of heterogeneity. Our scheme realizes the property of bloom filters, i.e., neighbor nodes can discover their shared keys but have no knowledge on the different keys possessed by the other node, without the probability of false positive. The analysis presented in this paper shows that our scheme has the ability to establish three different security level keys and achieves the property of self adaptive security for sensor networks with acceptable computation and communication consumption
Analysis of at CEPC
The rare decays are sensitive to contributions of new
physics (NP) and helpful to resolve the puzzle of multiple flavor
anomalies. In this work, we propose to study the
transition at a future lepton collider operating at the pole through the
decay. Using the decay form factors
from lattice simulations, we first update the SM prediction of BR( and the
corresponding longitudinal polarization fraction
. Our analysis uses the full CEPC simulation
samples with a net statistic of decays. Precise
and reconstructions are used to suppress backgrounds. The results show
that BR( can be measured with a statistical
uncertainty of and an ratio of at the
CEPC. The quality measures for the event reconstruction are also derived. By
combining the measurement of BR( and , the
constraints on the effective theory couplings at low energy are given.Comment: 12 pages, 15 figures, 3 table
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