118 research outputs found
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available
REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
The ability to detect and analyze failed executions automatically is crucial
for an explainable and robust robotic system. Recently, Large Language Models
(LLMs) have demonstrated strong common sense reasoning skills on textual
inputs. To leverage the power of LLM for robot failure explanation, we propose
a framework REFLECT, which converts multi-sensory data into a hierarchical
summary of robot past experiences and queries LLM with a progressive failure
explanation algorithm. Conditioned on the explanation, a failure correction
planner generates an executable plan for the robot to correct the failure and
complete the task. To systematically evaluate the framework, we create the
RoboFail dataset and show that our LLM-based framework is able to generate
informative failure explanations that assist successful correction planning.
Project website: https://roboreflect.github.io
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques
Real-time safety assessment (RTSA) of dynamic systems is a critical task that
has significant implications for various fields such as industrial and
transportation applications, especially in non-stationary environments.
However, the absence of a comprehensive review of real-time safety assessment
methods in non-stationary environments impedes the progress and refinement of
related methods. In this paper, a review of methods and techniques for RTSA
tasks in non-stationary environments is provided. Specifically, the background
and significance of RTSA approaches in non-stationary environments are firstly
highlighted. We then present a problem description that covers the definition,
classification, and main challenges. We review recent developments in related
technologies such as online active learning, online semi-supervised learning,
online transfer learning, and online anomaly detection. Finally, we discuss
future outlooks and potential directions for further research. Our review aims
to provide a comprehensive and up-to-date overview of real-time safety
assessment methods in non-stationary environments, which can serve as a
valuable resource for researchers and practitioners in this field.Comment: Accepted by the 2023 CAA Symposium on Fault Detection, Supervision
and Safety for Technical Processes (SAFEPROCESS 2023
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger
Robotic fingers made of soft material and compliant structures usually lead
to superior adaptation when interacting with the unstructured physical
environment. In this paper, we present an embedded sensing solution using
optical fibers for an omni-adaptive soft robotic finger with exceptional
adaptation in all directions. In particular, we managed to insert a pair of
optical fibers inside the finger's structural cavity without interfering with
its adaptive performance. The resultant integration is scalable as a versatile,
low-cost, and moisture-proof solution for physically safe human-robot
interaction. In addition, we experimented with our finger design for an object
sorting task and identified sectional diameters of 94\% objects within the
6mm error and measured 80\% of the structural strains within 0.1mm/mm
error. The proposed sensor design opens many doors in future applications of
soft robotics for scalable and adaptive physical interactions in the
unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE
RoboSoft 202
Modeling of a Microstrip Line Referenced to a Meshed Return Plane
Transmission Lines Referenced to Meshed Return Planes Are Widely Used Because of the Physical Flexibility Imparted by the Meshed Plane. Poor Accounting for the Meshed Ground, However, Can Lead to Severe Signal Integrity and Radio Frequency Interference Issues. Full-Wave Simulation Can Characterize the Electrical Performance at an Early Design Stage, But It is Both Time and Computational Resource Consuming. to Make the Simulation More Efficient, a Method is Proposed in This Study to Model Transmission Lines with a Meshed Reference Ground using 2D Analysis. the 2D Analysis is Performed at Several Locations Along the Length of the Trace above the Meshed Return to Determine Per-Unit-Length RLGC Parameters and Partial Self - and Mutual-Inductances of the Trace and Meshed Return. the Partial Self-Inductance of the Return is Then Corrected to Account for the Current Direction Along the Mesh. Cascading the Corrected S-Parameters for Each Segment is Then Used to Estimate the overall Characteristics of the Transmission Line. Results Found using This Approach Closely Match Those Found with 3D Full-Wave Simulation
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing
As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise
Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to
traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital
service quality assurance and security management method for communication
networks, which has become a crucial functional entity in 5G CPE/HGU. In recent
years, many researchers have applied Machine Learning or Deep Learning (DL) to
TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges,
including data dependency, resource-intensive traffic labeling, and user
privacy concerns. The limited computing resources of 5G CPE further complicate
efficient classification. Moreover, the "black box" nature of AI-TC models
raises transparency and credibility issues. The paper proposes the FedEdge
AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in
5G CPE. FL ensures privacy by employing local training, model parameter
iteration, and centralized training. A semi-supervised TC algorithm based on
Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces
data dependency while maintaining accuracy. To optimize model light-weight
deployment, the paper introduces XAI-Pruning, an AI model compression method
combined with DL model interpretability. Experimental evaluation demonstrates
FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient
TC performance. The framework enhances user privacy and model credibility,
offering a comprehensive solution for dependable and transparent Network TC in
5G CPE, thus enhancing service quality and security.Comment: 13 pages, 13 figure
Extraction of Stripline Surface Roughness using Cross-Section Information and S-Parameter Measurements
To characterize additional conductor loss introduced by conductor surface roughness, various models have been proposed to describe the relationship between foil roughness levels and surface roughness correction factor. However, all these empirical or physical models require a PCB sample to be manufactured and analyzed in advance. The procedure requires dissecting the PCB and is time- and labor-consuming. To avoid such a process, a new surface roughness extraction process is proposed here. Only the measured S-parameter and nominal cross-sectional information of the board are needed to extract the roughness level of conductor foils. Besides, this method can also deal with boards having non-equal roughness on different conductor surfaces, which is common in the manufactured printed circuit boards (PCB). The roughness level on each surface can be extracted separately to accurately model their contribution to the total conductor loss. The presented method is validated by both simulation and measurement. A good correlation is achieved between extracted roughness level and the measured value from the microscope
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