55 research outputs found
Investigation on the Performance of a Torque-driven Undulatory Swimmer with Distributed Flexibility
The current study presents a systematic investigation of the locomotion
performance of a swimmer with a wide range of parameter settings.
Two-dimensional simulations with the immersed boundary method are employed for
the fluid-structure interaction analysis. Unlike most previous studies where
the kinematics of the swimmer is predetermined, the locomotion of the current
swimmer is the response of a single periodic torque applied on the anterior
part. The effect of the distribution of body stiffness on swimming performance
and propulsion generation is discussed with different pitch frequencies and
amplitudes. An analysis of the phase-averaged vorticity field and thrust
sequence is given to clarify the change of performance due to the variation of
flexibility. This study demonstrates that body stiffness is a key factor that
influences the performance of undulatory swimming when the pitch angle is low
or moderate. The simple torque input of the current simulations provides a more
direct and engineering-related insight for the future design of microrobotic
swimmers
An investigation of information flux between turbulent boundary layer and porous medium
The interaction between boundary layer turbulence and a porous layer is the
cornerstone to the interface engineering. In this study, the spatial resolved
transfer entropy is used to assess the asymmetry of the causal interaction next
to a permeable wall. The analysis was based on pore-resolved direct numerical
simulation of turbulent channel flow over a cylinder array. The spatial map of
transfer entropy reveals the information flux between the porous medium and
arbitrary nearby position
Multi-Agent Consensus Seeking via Large Language Models
Multi-agent systems driven by large language models (LLMs) have shown
promising abilities for solving complex tasks in a collaborative manner. This
work considers a fundamental problem in multi-agent collaboration: consensus
seeking. When multiple agents work together, we are interested in how they can
reach a consensus through inter-agent negotiation. To that end, this work
studies a consensus-seeking task where the state of each agent is a numerical
value and they negotiate with each other to reach a consensus value. It is
revealed that when not explicitly directed on which strategy should be adopted,
the LLM-driven agents primarily use the average strategy for consensus seeking
although they may occasionally use some other strategies. Moreover, this work
analyzes the impact of the agent number, agent personality, and network
topology on the negotiation process. The findings reported in this work can
potentially lay the foundations for understanding the behaviors of LLM-driven
multi-agent systems for solving more complex tasks. Furthermore, LLM-driven
consensus seeking is applied to a multi-robot aggregation task. This
application demonstrates the potential of LLM-driven agents to achieve
zero-shot autonomous planning for multi-robot collaboration tasks. Project
website: westlakeintelligentrobotics.github.io/ConsensusLLM/
Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation
For many practical applications in wireless communications, we need to
recover a structured sparse signal from a linear observation model with dynamic
grid parameters in the sensing matrix. Conventional expectation maximization
(EM)-based compressed sensing (CS) methods, such as turbo compressed sensing
(Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have
double-loop iterations, where the inner loop (E-step) obtains a Bayesian
estimation of sparse signals and the outer loop (M-step) obtains a point
estimation of dynamic grid parameters. This leads to a slow convergence rate.
Furthermore, each iteration of the E-step involves a complicated matrix inverse
in general. To overcome these drawbacks, we first propose a successive linear
approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of
both sparse signals and dynamic grid parameters. Besides, we simplify the
matrix inverse operation based on the majorization-minimization (MM)
algorithmic framework. In addition, we extend our proposed algorithm from an
independent sparse prior to more complicated structured sparse priors, which
can exploit structured sparsity in specific applications to further enhance the
performance. Finally, we apply our proposed algorithm to solve two practical
application problems in wireless communications and verify that the proposed
algorithm can achieve faster convergence, lower complexity, and better
performance compared to the state-of-the-art EM-based methods.Comment: 13 pages, 17 figures, submitted to IEEE Transactions on Wireless
Communication
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Deep Neural Networks (DNNs) are expected to provide explanation for users to
understand their black-box predictions. Saliency map is a common form of
explanation illustrating the heatmap of feature attributions, but it suffers
from noise in distinguishing important features. In this paper, we propose a
model-agnostic learning method called Saliency Constrained Adaptive Adversarial
Training (SCAAT) to improve the quality of such DNN interpretability. By
constructing adversarial samples under the guidance of saliency map, SCAAT
effectively eliminates most noise and makes saliency maps sparser and more
faithful without any modification to the model architecture. We apply SCAAT to
multiple DNNs and evaluate the quality of the generated saliency maps on
various natural and pathological image datasets. Evaluations on different
domains and metrics show that SCAAT significantly improves the interpretability
of DNNs by providing more faithful saliency maps without sacrificing their
predictive power
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
This paper considers an integrated sensing and communication system, where
some radar targets also serve as communication scatterers. A location domain
channel modeling method is proposed based on the position of targets and
scatterers in the scattering environment, and the resulting radar and
communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We
propose a joint scattering environment sensing and channel estimation scheme to
enhance the target/scatterer localization and channel estimation performance
simultaneously, where a spatially non-stationary Markov random field (MRF)
model is proposed to capture the 2-D joint burst sparsity. An expectation
maximization (EM) based method is designed to solve the joint estimation
problem, where the E-step obtains the Bayesian estimation of the radar and
communication channels and the M-step automatically learns the dynamic position
grid and prior parameters in the MRF. However, the existing sparse Bayesian
inference methods used in the E-step involve a high-complexity matrix inverse
per iteration. Moreover, due to the complicated non-stationary MRF prior, the
complexity of M-step is exponentially large. To address these difficulties, we
propose an inverse-free variational Bayesian inference algorithm for the E-step
and a low-complexity method based on pseudo-likelihood approximation for the
M-step. In the simulations, the proposed scheme can achieve a better
performance than the state-of-the-art method while reducing the computational
overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless
Communication
What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
Pathological captioning of Whole Slide Images (WSIs), though is essential in
computer-aided pathological diagnosis, has rarely been studied due to the
limitations in datasets and model training efficacy. In this paper, we propose
a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological
captioning based on Transformers, which treats a WSI as a sequence of sparse
patches and generates an overall caption sentence from the sequence. An
accompanying subtype prediction is introduced into SGMT to guide the training
process and enhance the captioning accuracy. We also present an Asymmetric
Masked Mechansim approach to tackle the large size constraint of pathological
image captioning, where the numbers of sequencing patches in SGMT are sampled
differently in the training and inferring phases, respectively. Experiments on
the PatchGastricADC22 dataset demonstrate that our approach effectively adapts
to the task with a transformer-based model and achieves superior performance
than traditional RNN-based methods. Our codes are to be made available for
further research and development
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
Deep learning in digital pathology brings intelligence and automation as
substantial enhancements to pathological analysis, the gold standard of
clinical diagnosis. However, multiple steps from tissue preparation to slide
imaging introduce various image corruptions, making it difficult for deep
neural network (DNN) models to achieve stable diagnostic results for clinical
use. In order to assess and further enhance the robustness of the models, we
analyze the physical causes of the full-stack corruptions throughout the
pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE)
method to reproduce 21 types of corruptions quantified with 5-level severity.
We then construct three OmniCE-corrupted benchmark datasets at both patch level
and slide level and assess the robustness of popular DNNs in classification and
segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as
augmentation data for training and experiments to verify that the
generalization ability of the models has been significantly enhanced
Behavior of wire arc additively manufactured 316L austenitic stainless steel single shear bolted connections
This paper aims to investigate the behavior of single shear bolted connections made of wire arc additively manufactured 316L austenitic stainless steel. A set of 44 wire arc additive manufacturing (WAAM) 316L austenitic stainless steel single shear bolted connections were included with consideration of various bolt positions, surface conditions and loading orientations respective to the printing layer direction. The geometric dimensions of the WAAM austenitic stainless steel plates were measured with the assistance of non-contact 3D laser scanning prior to tensile testing. Monotonic tensile tests were carried out to investigate the load-deformation responses, failure patterns and resistances determined by both the deformation and strength criteria of the single shear bolted connections. The effects of geometric and printing parameters on the single shear bolted connections were analyzed. Due to the absence of codified design provisions for WAAM austenitic stainless steel bolted connections, the suitability of the existing design rules originally developed for traditionally manufactured carbon steel and stainless steel bolted connections was examined. Design resistances calculated by the Eurocode 3 (prEN 1993-1-8 and prEN 1993-1-4), the American Specification (ANSI/AISC 370-21) as well as the prevalent design recommendations proposed in existing literature were compared with the obtained experimental results. It is shown that the abovementioned design methods offer conservative predictions for the resistances of WAAM 316L austenitic stainless steel single shear bolted connections. Further study is needed to improve the accuracy of design resistance predictions
- …