338 research outputs found
Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks
Determination of boiling range of xylene mixed in PX device is currently a
crucial topic in the practical applications because of the recent disputes of
PX project in China. In our study, instead of determining the boiling range of
xylene mixed by traditional approach in laboratory or industry, we successfully
established two Artificial Neural Networks (ANNs) models to determine the
initial boiling point and final boiling point respectively. Results show that
the Multilayer Feedforward Neural Networks (MLFN) model with 7 nodes (MLFN-7)
is the best model to determine the initial boiling point of xylene mixed, with
the RMS error 0.18; while the MLFN model with 4 nodes (MLFN-4) is the best
model to determine the final boiling point of xylene mixed, with the RMS error
0.75. The training and testing processes both indicate that the models we
developed are robust and precise. Our research can effectively avoid the damage
of the PX device to human body and environment
The optimal connection model for blood vessels segmentation and the MEA-Net
Vascular diseases have long been regarded as a significant health concern.
Accurately detecting the location, shape, and afflicted regions of blood
vessels from a diverse range of medical images has proven to be a major
challenge. Obtaining blood vessels that retain their correct topological
structures is currently a crucial research issue. Numerous efforts have sought
to reinforce neural networks' learning of vascular geometric features,
including measures to ensure the correct topological structure of the
segmentation result's vessel centerline. Typically, these methods extract
topological features from the network's segmentation result and then apply
regular constraints to reinforce the accuracy of critical components and the
overall topological structure. However, as blood vessels are three-dimensional
structures, it is essential to achieve complete local vessel segmentation,
which necessitates enhancing the segmentation of vessel boundaries.
Furthermore, current methods are limited to handling 2D blood vessel
fragmentation cases. Our proposed boundary attention module directly extracts
boundary voxels from the network's segmentation result. Additionally, we have
established an optimal connection model based on minimal surfaces to determine
the connection order between blood vessels. Our method achieves
state-of-the-art performance in 3D multi-class vascular segmentation tasks, as
evidenced by the high values of Dice Similarity Coefficient (DSC) and
Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the
Betti error, LR error, and BR error indicators of vessel richness and
structural integrity by more than 10% compared to other methods, and
effectively addresses vessel fragmentation and yields blood vessels with a more
precise topological structure.Comment: 19 page
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on
improving the accuracy of the detection, while largely ignoring the
explainability of the corresponding methods and thus leaving the explanation of
outcomes to practitioners. As anomaly detection algorithms are increasingly
used in safety-critical domains, providing explanations for the high-stakes
decisions made in those domains has become an ethical and regulatory
requirement. Therefore, this work provides a comprehensive and structured
survey on state-of-the-art explainable anomaly detection techniques. We propose
a taxonomy based on the main aspects that characterize each explainable anomaly
detection technique, aiming to help practitioners and researchers find the
explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from
Data (TKDD) for publication (preprint version
Test-Time Training for Semantic Segmentation with Output Contrastive Loss
Although deep learning-based segmentation models have achieved impressive
performance on public benchmarks, generalizing well to unseen environments
remains a major challenge. To improve the model's generalization ability to the
new domain during evaluation, the test-time training (TTT) is a challenging
paradigm that adapts the source-pretrained model in an online fashion. Early
efforts on TTT mainly focus on the image classification task. Directly
extending these methods to semantic segmentation easily experiences unstable
adaption due to segmentation's inherent characteristics, such as extreme class
imbalance and complex decision spaces. To stabilize the adaptation process, we
introduce contrastive loss (CL), known for its capability to learn robust and
generalized representations. Nevertheless, the traditional CL operates in the
representation space and cannot directly enhance predictions. In this paper, we
resolve this limitation by adapting the CL to the output space, employing a
high temperature, and simplifying the formulation, resulting in a
straightforward yet effective loss function called Output Contrastive Loss
(OCL). Our comprehensive experiments validate the efficacy of our approach
across diverse evaluation scenarios. Notably, our method excels even when
applied to models initially pre-trained using domain adaptation methods on test
domain data, showcasing its resilience and adaptability.\footnote{Code and more
information could be found at~ \url{https://github.com/dazhangyu123/OCL}
General synthesis of porous mixed metal oxide hollow spheres with enhanced supercapacitive properties
Porous mixed metal oxide (MMO) hollow spheres present high specific surface areas, abundant electrochemically active sites, and outstanding electrochemical properties, showing potential applications in energy storage. A hydro/solvothermal process, followed by a calcination process, can be a viable method for producing uniform porous metal oxide hollow spheres. Unfortunately, this method usually involves harsh synthetic conditions such as high temperature and intricate processing. Herein, we report a general and facile ion adsorption-annealing approach for the fabrication of uniform porous MMO hollow spheres. The size and shell thickness of the as-obtained hollow spheres can be adjusted by the carbohydrate sphere templates and the solution concentration. Electrochemical measurements of the MMO hollow spheres demonstrate excellent supercapacitive properties, which may be due to the small size, ultrathin shells, and fine porous structure
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
As an emerging technology and a relatively affordable device, the 4D imaging
radar has already been confirmed effective in performing 3D object detection in
autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point
clouds hinder further performance improvement, and in-depth studies about its
fusion with other modalities are lacking. On the other hand, most of the
camera-based perception methods transform the extracted image perspective view
features into the bird's-eye view geometrically via "depth-based splatting"
proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals
such as LiDARs or ordinary automotive radars for enhancement. Recently, a few
works have applied the "sampling" strategy for image view transformation,
showing that it outperforms "splatting" even without image depth prediction.
However, the potential of "sampling" is not fully unleashed. In this paper, we
investigate the "sampling" view transformation strategy on the camera and 4D
imaging radar fusion-based 3D object detection. In the proposed model, LXL,
predicted image depth distribution maps and radar 3D occupancy grids are
utilized to aid image view transformation, called "radar occupancy-assisted
depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the
proposed method outperforms existing 3D object detection methods by a
significant margin without bells and whistles. Ablation studies demonstrate
that our method performs the best among different enhancement settings
Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?
Online 3D multi-object tracking (MOT) has recently received significant
research interests due to the expanding demand of 3D perception in advanced
driver assistance systems (ADAS) and autonomous driving (AD). Among the
existing 3D MOT frameworks for ADAS and AD, conventional point object tracking
(POT) framework using the tracking-by-detection (TBD) strategy has been well
studied and accepted for LiDAR and 4D imaging radar point clouds. In contrast,
extended object tracking (EOT), another important framework which accepts the
joint-detection-and-tracking (JDT) strategy, has rarely been explored for
online 3D MOT applications. This paper provides the first systematical
investigation of the EOT framework for online 3D MOT in real-world ADAS and AD
scenarios. Specifically, the widely accepted TBD-POT framework, the recently
investigated JDT-EOT framework, and our proposed TBD-EOT framework are compared
via extensive evaluations on two open source 4D imaging radar datasets:
View-of-Delft and TJ4DRadSet. Experiment results demonstrate that the
conventional TBD-POT framework remains preferable for online 3D MOT with high
tracking performance and low computational complexity, while the proposed
TBD-EOT framework has the potential to outperform it in certain situations.
However, the results also show that the JDT-EOT framework encounters multiple
problems and performs inadequately in evaluation scenarios. After analyzing the
causes of these phenomena based on various evaluation metrics and
visualizations, we provide possible guidelines to improve the performance of
these MOT frameworks on real-world data. These provide the first benchmark and
important insights for the future development of 4D imaging radar-based online
3D MOT.Comment: 8 pages, 5 figures, submitted to the 2024 IEEE International
Conference on Robotics and Automation (ICRA2024
ControlCom: Controllable Image Composition using Diffusion Model
Image composition targets at synthesizing a realistic composite image from a
pair of foreground and background images. Recently, generative composition
methods are built on large pretrained diffusion models to generate composite
images, considering their great potential in image generation. However, they
suffer from lack of controllability on foreground attributes and poor
preservation of foreground identity. To address these challenges, we propose a
controllable image composition method that unifies four tasks in one diffusion
model: image blending, image harmonization, view synthesis, and generative
composition. Meanwhile, we design a self-supervised training framework coupled
with a tailored pipeline of training data preparation. Moreover, we propose a
local enhancement module to enhance the foreground details in the diffusion
model, improving the foreground fidelity of composite images. The proposed
method is evaluated on both public benchmark and real-world data, which
demonstrates that our method can generate more faithful and controllable
composite images than existing approaches. The code and model will be available
at https://github.com/bcmi/ControlCom-Image-Composition
- …