455 research outputs found
Sensitivity analysis of leaf blower vibration isolator based on ISIGHT
In order to reduce the optimal design space of the vibration isolator, a sensitivity analysis method based on ISIGHT for the leaf blower vibration isolator was proposed in this paper. Parametric modeling of the isolator was realized by Solidworks, Hypermesh, Optistruct and ADAMS, and a multi-software co-simulation working framework based on ISIGHT was established. Taking the design parameters of the vibration isolator as the variable and the minimum transmission force as the optimization, Sensitivity analysis of the vibration isolator was carried out based on the Latin Hypercube. The results show that the length of the vibration isolator has the greatest influence on the vibration isolation and the contribution of d1, d3, D3 and E3 is the smallest. The result provided a guiding significance for the design optimization of the vibration isolator
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Iron and nickel doped CoSe2 as efficient non precious metal catalysts for oxygen reduction
Iron and nickel doped CoSe2 were prepared by solvothermal method, and they were proved to be ternary chalcogenides by series of physical characterization. The effects of the iron and nickel contents on the oxygen reduction reaction were investigated by electrochemical measurements, and the highest activities were obtained on Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2, respectively. Both Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2 presented four-electron pathway. Furthermore, Co0.7Fe0.3Se2 exhibited more positive cathodic peak potential (0.564 V) and onset potential (0.759 V) than these of Co0.7Ni0.3Se2 (0.558 V and 0.741 V). And Co0.7Fe0.3Se2 displayed even superior stability and better tolerance to methanol, ethanol and ethylene glycol crossover effects than the commercial Pt/C (20 wt% Pt)
Family Health Monitoring System Based on the Four Sessions Internet of Things
The accelerating pace of modern life results in the lack of effective care of people’s health status. Nowadays, resorting to the technology of the Internet of Things, we can provide home health monitoring services to minimize the impact of the disease brought to people. In this article, we proposed the realization method for the architecture of the four sections of the Internet of Things oriented to home health monitoring service, furthermore, the secondary the smoothness index method is applied to the monitoring of human health index, data from body temperature detection experiments verified the feasibility of the four sessions system, which laid firm foundations for the requirement of real-time and accuracy of the Internet of Things based home health monitoring system with a common reference significance and value in use
Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence
Existing gait recognition frameworks retrieve an identity in the gallery
based on the distance between a probe sample and the identities in the gallery.
However, existing methods often neglect that the gallery may not contain
identities corresponding to the probes, leading to recognition errors rather
than raising an alarm. In this paper, we introduce a novel uncertainty-aware
gait recognition method that models the uncertainty of identification based on
learned evidence. Specifically, we treat our recognition model as an evidence
collector to gather evidence from input samples and parameterize a Dirichlet
distribution over the evidence. The Dirichlet distribution essentially
represents the density of the probability assigned to the input samples. We
utilize the distribution to evaluate the resultant uncertainty of each probe
sample and then determine whether a probe has a counterpart in the gallery or
not. To the best of our knowledge, our method is the first attempt to tackle
gait recognition with uncertainty modelling. Moreover, our uncertain modeling
significantly improves the robustness against out-of-distribution (OOD)
queries. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on datasets with OOD queries, and can also
generalize well to other identity-retrieval tasks. Importantly, our method
outperforms the state-of-the-art by a large margin of 51.26% when the OOD query
rate is around 50% on OUMVLP
DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
Gait recognition is a biometric technology that recognizes the identity of
humans through their walking patterns. Compared with other biometric
technologies, gait recognition is more difficult to disguise and can be applied
to the condition of long-distance without the cooperation of subjects. Thus, it
has unique potential and wide application for crime prevention and social
security. At present, most gait recognition methods directly extract features
from the video frames to establish representations. However, these
architectures learn representations from different features equally but do not
pay enough attention to dynamic features, which refers to a representation of
dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the
human body are more informative than other parts (e.g. bags) during walking, in
this paper, we propose a novel and high-performance framework named DyGait.
This is the first framework on gait recognition that is designed to focus on
the extraction of dynamic features. Specifically, to take full advantage of the
dynamic information, we propose a Dynamic Augmentation Module (DAM), which can
automatically establish spatial-temporal feature representations of the dynamic
parts of the human body. The experimental results show that our DyGait network
outperforms other state-of-the-art gait recognition methods. It achieves an
average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D
dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
Many gait recognition methods first partition the human gait into N-parts and
then combine them to establish part-based feature representations. Their gait
recognition performance is often affected by partitioning strategies, which are
empirically chosen in different datasets. However, we observe that strips as
the basic component of parts are agnostic against different partitioning
strategies. Motivated by this observation, we present a strip-based multi-level
gait recognition network, named GaitStrip, to extract comprehensive gait
information at different levels. To be specific, our high-level branch explores
the context of gait sequences and our low-level one focuses on detailed posture
changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the
strip-based feature representations by directly taking each strip of the human
body as the basic unit. Moreover, we propose a novel multi-branch structure,
called Enhanced Convolution Module (ECM), to extract different representations
of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the
Frame-Level feature extractor (FL) and SPB, and has two obvious advantages:
First, each branch focuses on a specific representation, which can be used to
improve the robustness of the network. Specifically, ST aims to extract
spatial-temporal features of gait sequences, while FL is used to generate the
feature representation of each frame. Second, the parameters of the ECM can be
reduced in test by introducing a structural re-parameterization technique.
Extensive experimental results demonstrate that our GaitStrip achieves
state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202
EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration
In multimodal perception systems, achieving precise extrinsic calibration
between LiDAR and camera is of critical importance. Previous calibration
methods often required specific targets or manual adjustments, making them both
labor-intensive and costly. Online calibration methods based on features have
been proposed, but these methods encounter challenges such as imprecise feature
extraction, unreliable cross-modality associations, and high scene-specific
requirements. To address this, we introduce an edge-based approach for
automatic online calibration of LiDAR and cameras in real-world scenarios. The
edge features, which are prevalent in various environments, are aligned in both
images and point clouds to determine the extrinsic parameters. Specifically,
stable and robust image edge features are extracted using a SAM-based method
and the edge features extracted from the point cloud are weighted through a
multi-frame weighting strategy for feature filtering. Finally, accurate
extrinsic parameters are optimized based on edge correspondence constraints. We
conducted evaluations on both the KITTI dataset and our dataset. The results
show a state-of-the-art rotation accuracy of 0.086{\deg} and a translation
accuracy of 0.977 cm, outperforming existing edge-based calibration methods in
both precision and robustness
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