161 research outputs found
Recommended from our members
Microstructure Control and Performance Evolution of Aluminum Alloy 7075 by Nano-Treating.
Nano-treating is a novel concept wherein a low percentage of nanoparticles is used for microstructural control and property tuning in metals and alloys. The nano-treating of AA7075 was investigated to control its microstructure and improve its structural stability for high performance. After treatment with TiC nanoparticles, the grains were significantly refined from coarse dendrites of hundreds of micrometers to fine equiaxial ones smaller than 20 μm. After T6 heat treatment, the grains, with an average size of 18.5 μm, remained almost unchanged, demonstrating an excellent thermal stability. It was found that besides of growth restriction factor by pinning behavior on grain boundries, TiC nanoparticles served as both an effective nucleation agent for primary grains and an effective secondary phase modifier in AA7075. Furthermore, the mechanical properties of nano-treated AA7075 were improved over those of the pure alloy. Thus, nano-treating provides a new method to enhance the performance of aluminum alloys for numerous applications
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of
attention in the field of image generation recently, thanks to its powerful
generation capability. However, diffusion models have not yet received
sufficient research in the field of image fusion. In this article, we introduce
diffusion model to the image fusion field, treating the image fusion task as
image-to-image translation and designing two different conditional injection
modulation modules (i.e., style transfer modulation and wavelet modulation) to
inject coarse-grained style information and fine-grained high-frequency and
low-frequency information into the diffusion UNet, thereby generating fused
images. In addition, we also discussed the residual learning and the selection
of training objectives of the diffusion model in the image fusion task.
Extensive experimental results based on quantitative and qualitative
assessments compared with benchmarks demonstrates state-of-the-art results and
good generalization performance in image fusion tasks. Finally, it is hoped
that our method can inspire other works and gain insight into this field to
better apply the diffusion model to image fusion tasks. Code shall be released
for better reproducibility
Robot Task Planning and Situation Handling in Open Worlds
Automated task planning algorithms have been developed to help robots
complete complex tasks that require multiple actions. Most of those algorithms
have been developed for "closed worlds" assuming complete world knowledge is
provided. However, the real world is generally open, and the robots frequently
encounter unforeseen situations that can potentially break the planner's
completeness. This paper introduces a novel algorithm (COWP) for open-world
task planning and situation handling that dynamically augments the robot's
action knowledge with task-oriented common sense. In particular, common sense
is extracted from Large Language Models based on the current task at hand and
robot skills. For systematic evaluations, we collected a dataset that includes
561 execution-time situations in a dining domain, where each situation
corresponds to a state instance of a robot being potentially unable to complete
a task using a solution that normally works. Experimental results show that our
approach significantly outperforms competitive baselines from the literature in
the success rate of service tasks. Additionally, we have demonstrated COWP
using a mobile manipulator. Supplementary materials are available at:
https://cowplanning.github.io
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Task planning systems have been developed to help robots use human knowledge
(about actions) to complete long-horizon tasks. Most of them have been
developed for "closed worlds" while assuming the robot is provided with
complete world knowledge. However, the real world is generally open, and the
robots frequently encounter unforeseen situations that can potentially break
the planner's completeness. Could we leverage the recent advances on
pre-trained Large Language Models (LLMs) to enable classical planning systems
to deal with novel situations?
This paper introduces a novel framework, called COWP, for open-world task
planning and situation handling. COWP dynamically augments the robot's action
knowledge, including the preconditions and effects of actions, with
task-oriented commonsense knowledge. COWP embraces the openness from LLMs, and
is grounded to specific domains via action knowledge. For systematic
evaluations, we collected a dataset that includes 1,085 execution-time
situations. Each situation corresponds to a state instance wherein a robot is
potentially unable to complete a task using a solution that normally works.
Experimental results show that our approach outperforms competitive baselines
from the literature in the success rate of service tasks. Additionally, we have
demonstrated COWP using a mobile manipulator. Supplementary materials are
available at: https://cowplanning.github.io/Comment: arXiv admin note: substantial text overlap with arXiv:2210.0128
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
Web applications are increasingly becoming the primary platform for AI
service delivery, making in-browser deep learning (DL) inference more
prominent. However, current in-browser inference systems fail to effectively
utilize advanced web programming techniques and customize kernels for various
client devices, leading to suboptimal performance.
To address the issues, this paper presents the first in-browser inference
system, nn-JIT.web, which enables just-in-time (JIT) auto-generation of
optimized kernels for both CPUs and GPUs during inference. The system achieves
this by using two novel web programming techniques that can significantly
reduce kernel generation time, compared to other tensor compilers such as TVM,
while maintaining or even improving performance. The first technique,
Tensor-Web Compiling Co-Design, lowers compiling costs by unifying tensor and
web compiling and eliminating redundant and ineffective compiling passes. The
second technique, Web-Specific Lite Kernel Optimization Space Design, reduces
kernel tuning costs by focusing on web programming requirements and efficient
hardware resource utilization, limiting the optimization space to only dozens.
nn-JIT.web is evaluated for modern transformer models on a range of client
devices, including the mainstream CPUs and GPUs from ARM, Intel, AMD and
Nvidia. Results show that nn-JIT.web can achieve up to 8.2x faster within 30
seconds compared to the baselines across various models
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
Multi-agent exploration of a bounded 3D environment with unknown initial
positions of agents is a challenging problem. It requires quickly exploring the
environments as well as robustly merging the sub-maps built by the agents. We
take the view that the existing approaches are either aggressive or
conservative: Aggressive strategies merge two sub-maps built by different
agents together when overlap is detected, which can lead to incorrect merging
due to the false-positive detection of the overlap and is thus not robust.
Conservative strategies direct one agent to revisit an excessive amount of the
historical trajectory of another agent for verification before merging, which
can lower the exploration efficiency due to the repeated exploration of the
same space. To intelligently balance the robustness of sub-map merging and
exploration efficiency, we develop a new approach for lidar-based multi-agent
exploration, which can direct one agent to repeat another agent's trajectory in
an \emph{adaptive} manner based on the quality indicator of the sub-map merging
process. Additionally, our approach extends the recent single-agent
hierarchical exploration strategy to multiple agents in a \emph{cooperative}
manner by planning for agents with merged sub-maps together to further improve
exploration efficiency. Our experiments show that our approach is up to 50\%
more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Training control policies in simulation is more appealing than on real robots
directly, as it allows for exploring diverse states in a safe and efficient
manner. Yet, robot simulators inevitably exhibit disparities from the real
world, yielding inaccuracies that manifest as the simulation-to-real gap.
Existing literature has proposed to close this gap by actively modifying
specific simulator parameters to align the simulated data with real-world
observations. However, the set of tunable parameters is usually manually
selected to reduce the search space in a case-by-case manner, which is hard to
scale up for complex systems and requires extensive domain knowledge. To
address the scalability issue and automate the parameter-tuning process, we
introduce an approach that aligns the simulator with the real world by
discovering the causal relationship between the environment parameters and the
sim-to-real gap. Concretely, our method learns a differentiable mapping from
the environment parameters to the differences between simulated and real-world
robot-object trajectories. This mapping is governed by a simultaneously-learned
causal graph to help prune the search space of parameters, provide better
interpretability, and improve generalization. We perform experiments to achieve
both sim-to-sim and sim-to-real transfer, and show that our method has
significant improvements in trajectory alignment and task success rate over
strong baselines in a challenging manipulation task
An improved model using convolutional sliding window-attention network for motor imagery EEG classification
IntroductionThe classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.MethodsTo solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.ResultsThe model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.DiscussionThe experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation
Food preference strategy of four sympatric rodents in a temperate forest in northeast China
Rodents are well known as both seed predators and dispersers of various plant species in forest ecosystems, and they play an important role in the regeneration of vegetation. Thus, the research on seed selection and vegetation regeneration by sympatric rodents is an interesting topic. To understand the characteristics of preferences of rodents for different seeds, a semi-natural enclosure experiment was performed with four rodent species (Apodemus peninsulae, Apodemus agrarius, Tscherskia triton, and Clethrionomys rufocanus) and the seeds of seven plant species (Pinus koraiensis, Corylus mandshurica, Quercus mongolica, Juglans mandshurica, Armeniaca sibirica, Prunus salicina, and Cerasus tomentosa) to investigate the differentiation in niches and patterns of resource utilization of sympatric rodents. The results showed that all the rodents had consumed many seeds of Pi. koraiensis, Co. mandshurica, and Q. mongolica but differed significantly in how they selected the different seeds. The rate of utilization (Ri) of Pi. koraiensis, Co. mandshurica, and Q. mongolica exhibited the highest values. The Ei values indicated that the rodents tested exhibited differences in their priorities used to select the seeds from different plant species. All four species of rodents exhibited obvious preferences for certain seeds. Korean field mice preferentially consumed the seeds of Q. mongolica, Co. mandshurica, and Pi. koraiensis. Striped field mice favor the seeds of Co. mandshurica, Q. mongolica, P. koraiensis, and Nanking cherry. Greater long-tailed hamsters prefer to consume the seeds of Pi. koraiensis, Co. mandshurica, Q. mongolica, Pr. salicina, and Ce. tomentosa. Clethrionomys rufocanus likes to eat the seeds of Pi. koraiensis, Q. mongolica, Co. mandshurica, and Ce. tomentosa. The results supported our hypothesis that sympatric rodents overlap in food selection. However, each rodent species has a marked preference for food selection, and different rodent species differ in their food preferences. This reflects the role of distinct food niche differentiation in their coexistence
- …