832 research outputs found
Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks
Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust
A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios
Embedding-aware generative model (EAGM) addresses the data insufficiency
problem for zero-shot learning (ZSL) by constructing a generator between
semantic and visual feature spaces. Thanks to the predefined benchmark and
protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue
that it is time to take a step back and reconsider the embedding-aware
generative paradigm. The main work of this paper is two-fold. First, the
embedding features in benchmark datasets are somehow overlooked, which
potentially limits the performance of EAGMs, while most researchers focus on
how to improve EAGMs. Therefore, we conduct a systematic evaluation of ten
representative EAGMs and prove that even embarrassedly simple modifications on
the embedding features can improve the performance of EAGMs for ZSL remarkably.
So it's time to pay more attention to the current embedding features in
benchmark datasets. Second, based on five benchmark datasets, each with six
any-shot learning scenarios, we systematically compare the performance of ten
typical EAGMs for the first time, and we give a strong baseline for zero-shot
learning (ZSL) and few-shot learning (FSL). Meanwhile, a comprehensive
generative model repository, namely, generative any-shot learning (GASL)
repository, is provided, which contains the models, features, parameters, and
scenarios of EAGMs for ZSL and FSL. Any results in this paper can be readily
reproduced with only one command line based on GASL
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization
Weakly supervised temporal action localization (WSTAL) aims to localize
actions in untrimmed videos using video-level labels. Despite recent advances,
existing approaches mainly follow a localization-by-classification pipeline,
generally processing each segment individually, thereby exploiting only limited
contextual information. As a result, the model will lack a comprehensive
understanding (e.g. appearance and temporal structure) of various action
patterns, leading to ambiguity in classification learning and temporal
localization. Our work addresses this from a novel perspective, by exploring
and exploiting the cross-video contextual knowledge within the dataset to
recover the dataset-level semantic structure of action instances via weak
labels only, thereby indirectly improving the holistic understanding of
fine-grained action patterns and alleviating the aforementioned ambiguities.
Specifically, an end-to-end framework is proposed, including a Robust
Memory-Guided Contrastive Learning (RMGCL) module and a Global Knowledge
Summarization and Aggregation (GKSA) module. First, the RMGCL module explores
the contrast and consistency of cross-video action features, assisting in
learning more structured and compact embedding space, thus reducing ambiguity
in classification learning. Further, the GKSA module is used to efficiently
summarize and propagate the cross-video representative action knowledge in a
learnable manner to promote holistic action patterns understanding, which in
turn allows the generation of high-confidence pseudo-labels for self-learning,
thus alleviating ambiguity in temporal localization. Extensive experiments on
THUMOS14, ActivityNet1.3, and FineAction demonstrate that our method
outperforms the state-of-the-art methods, and can be easily plugged into other
WSTAL methods.Comment: Submitted to TCSVT. 14 pages and 7 figure
Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis
Fault diagnosis is a critical aspect of industrial safety, and supervised
industrial fault diagnosis has been extensively researched. However, obtaining
fault samples of all categories for model training can be challenging due to
cost and safety concerns. As a result, the generalized zero-shot industrial
fault diagnosis has gained attention as it aims to diagnose both seen and
unseen faults. Nevertheless, the lack of unseen fault data for training poses a
challenging domain shift problem (DSP), where unseen faults are often
identified as seen faults. In this article, we propose a knowledge space
sharing (KSS) model to address the DSP in the generalized zero-shot industrial
fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and
a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by
recombining transferable attribute features extracted from seen samples under
the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with
the help of generated samples, which aims to address the DSP by modeling seen
categories in the knowledge space. KSS-D avoids misclassifying rare faults as
seen faults and identifies seen fault samples. We conduct generalized zero-shot
diagnosis experiments on the benchmark Tennessee-Eastman process, and our
results show that our approach outperforms state-of-the-art methods for the
generalized zero-shot industrial fault diagnosis problem
Light-LOAM: A Lightweight LiDAR Odometry and Mapping based on Graph-Matching
Simultaneous Localization and Mapping (SLAM) plays an important role in robot
autonomy. Reliability and efficiency are the two most valued features for
applying SLAM in robot applications. In this paper, we consider achieving a
reliable LiDAR-based SLAM function in computation-limited platforms, such as
quadrotor UAVs based on graph-based point cloud association. First, contrary to
most works selecting salient features for point cloud registration, we propose
a non-conspicuous feature selection strategy for reliability and robustness
purposes. Then a two-stage correspondence selection method is used to register
the point cloud, which includes a KD-tree-based coarse matching followed by a
graph-based matching method that uses geometric consistency to vote out
incorrect correspondences. Additionally, we propose an odometry approach where
the weight optimizations are guided by vote results from the aforementioned
geometric consistency graph. In this way, the optimization of LiDAR odometry
rapidly converges and evaluates a fairly accurate transformation resulting in
the back-end module efficiently finishing the mapping task. Finally, we
evaluate our proposed framework on the KITTI odometry dataset and real-world
environments. Experiments show that our SLAM system achieves a comparative
level or higher level of accuracy with more balanced computation efficiency
compared with the mainstream LiDAR-based SLAM solutions
Novel Combined Freeze-Drying and Instant Controlled Pressure Drop Drying for Restructured Carrot-Potato Chips: Optimized by Response Surface Method
Combined freeze-drying and instant controlled pressure drop process (FD-DIC) for restructured carrot-potato chips was developed and its processing conditions were optimized using response surface methodology (RSM) with the purpose of improving the quality of products and reducing energy consumption. Three critical variables including the amount of carrot, the moisture content of the partially dried product before DIC treatment, and equilibrium temperature of DIC for the restructured chips were considered. Response parameters such as the final moisture content, color value (L, a, and b), and texture properties of restructured carrot-potato chips were investigated. The results showed that the graphical optimal ranges of FD-DIC drying process were as follows: the amount of carrot was 46–54% w/w, the moisture content of the partially dried product before DIC treatment was 0.20–0.35 g/g, and the equilibrium temperature of DIC was 85–95°C. Furthermore, the numerical optimization suggested that conditions were 47.43% w/w, 0.29 g/g, and 90.57°C, respectively. It could be concluded that the combined drying method of FD-DIC provided the restructured carrot-potato chips with higher quality, as compared to the freeze-dried chips. Considering the relatively high production cost of FD, this novel FD-DIC could be an alternative method for obtaining desirable restructured fruit and vegetable chips
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained
Sentiment Analysis task, which has attracted growing research interests
recently. Existing work mainly utilizes image information to improve the
performance of MABSA task. However, most of the studies overestimate the
importance of images since there are many noise images unrelated to the text in
the dataset, which will have a negative impact on model learning. Although some
work attempts to filter low-quality noise images by setting thresholds, relying
on thresholds will inevitably filter out a lot of useful image information.
Therefore, in this work, we focus on whether the negative impact of noisy
images can be reduced without modifying the data. To achieve this goal, we
borrow the idea of Curriculum Learning and propose a Multi-grained
Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by
adjusting the order of training data. Extensive experimental results show that
our framework consistently outperforms state-of-the-art work on three sub-tasks
of MABSA.Comment: Accepted by EMNLP 202
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