1,115 research outputs found
Robust Component-based Network Localization with Noisy Range Measurements
Accurate and robust localization is crucial for wireless ad-hoc and sensor
networks. Among the localization techniques, component-based methods advance
themselves for conquering network sparseness and anchor sparseness. But
component-based methods are sensitive to ranging noises, which may cause a huge
accumulated error either in component realization or merging process. This
paper presents three results for robust component-based localization under
ranging noises. (1) For a rigid graph component, a novel method is proposed to
evaluate the graph's possible number of flip ambiguities under noises. In
particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR
(MIRROR)} is presented as the cause of flip ambiguity, and the number of
MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then
the sensitivity of a graph's local deforming regarding ranging noises is
investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM)
is proposed to estimate the node location perturbations due to ranging noises.
(3) By evaluating component robustness via the flipping and the local deforming
risks, a Robust Component Generation and Realization (RCGR) algorithm is
developed, which generates components based on the robustness metrics. RCGR was
evaluated by simulations, which showed much better noise resistance and
locating accuracy improvements than state-of-the-art of component-based
localization algorithms.Comment: 9 pages, 15 figures, ICCCN 2018, Hangzhou, Chin
Deep Multimodal Speaker Naming
Automatic speaker naming is the problem of localizing as well as identifying
each speaking character in a TV/movie/live show video. This is a challenging
problem mainly attributes to its multimodal nature, namely face cue alone is
insufficient to achieve good performance. Previous multimodal approaches to
this problem usually process the data of different modalities individually and
merge them using handcrafted heuristics. Such approaches work well for simple
scenes, but fail to achieve high performance for speakers with large appearance
variations. In this paper, we propose a novel convolutional neural networks
(CNN) based learning framework to automatically learn the fusion function of
both face and audio cues. We show that without using face tracking, facial
landmark localization or subtitle/transcript, our system with robust multimodal
feature extraction is able to achieve state-of-the-art speaker naming
performance evaluated on two diverse TV series. The dataset and implementation
of our algorithm are publicly available online
Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
In the machine learning domain, research on anomaly detection and
localization within image data has garnered significant attention, particularly
in practical applications such as industrial defect detection. While existing
approaches predominantly rely on Convolutional Neural Networks (CNN) as their
backbone network, we propose an innovative method based on the Transformer
backbone network. Our approach employs a two-stage incremental learning
strategy. In the first stage, we train a Masked Autoencoder (MAE) model
exclusively on normal images. Subsequently, in the second stage, we implement
pixel-level data augmentation techniques to generate corrupted normal images
and their corresponding pixel labels. This process enables the model to learn
how to repair corrupted regions and classify the state of each pixel.
Ultimately, the model produces a pixel reconstruction error matrix and a pixel
anomaly probability matrix, which are combined to create an anomaly scoring
matrix that effectively identifies abnormal regions. When compared to several
state-of-the-art CNN-based techniques, our method demonstrates superior
performance on the MVTec AD dataset, achieving an impressive 97.6% AUC
An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor
In a traditional lumped-parameter thermal network, no distinction is made between the heat and non-heat sources, resulting in both larger heat flux and temperature drop in the uniform heat source. In this paper, an improved lumped-parameter thermal network is proposed to deal with such problems. The innovative aspect of this proposed method is that it considers the influence of heat flux change in the heat source, and then gives a half-resistance theory for the heat source to achieve the temperature drop balance. In addition, the coupling relationship between the boundary temperature and loading position of the heat generator is also added in the lumped-parameter thermal network, so as to amend the loading position and nodes’ temperature through iterations. This approach breaks the limitation of the traditional lumped-parameter thermal network: that the heat generator can only be loaded at the midpoint, which is critical to determining the maximum temperature in asymmetric heat dissipation. By adjusting the location of heat generator and thermal resistances of each branch, the accuracy of temperature prediction is further improved. A simulation and an experiment on a U-core motor show that the improved lumped-parameter thermal network not only achieves higher accuracy than the traditional one, but also determines the loading position of the heat generator well
Magnetic Proximity Effect and Interlayer Exchange Coupling of Ferromagnetic/Topological Insulator/Ferromagnetic Trilayer
Magnetic proximity effect between topological insulator (TI) and
ferromagnetic insulator (FMI) is considered to have great potential in
spintronics. However, a complete determination of interfacial magnetic
structure has been highly challenging. We theoretically investigate the
interlayer exchange coupling of two FMIs separated by a TI thin film, and show
that the particular electronic states of the TI contributing to the proximity
effect can be directly identified through the coupling behavior between two
FMIs, together with a tunability of coupling constant. Such FMI/TI/FMI
structure not only serves as a platform to clarify the magnetic structure of
FMI/TI interface, but also provides insights into designing the magnetic
storage devices with ultrafast response.Comment: 7 pages, 4 figure
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