56 research outputs found
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
In recent times, Large Language Models (LLMs) have captured a global
spotlight and revolutionized the field of Natural Language Processing. One of
the factors attributed to the effectiveness of LLMs is the model architecture
used for training, transformers. Transformer models excel at capturing
contextual features in sequential data since time series data are sequential,
transformer models can be leveraged for more efficient time series data
prediction. The field of prognostics is vital to system health management and
proper maintenance planning. A reliable estimation of the remaining useful life
(RUL) of machines holds the potential for substantial cost savings. This
includes avoiding abrupt machine failures, maximizing equipment usage, and
serving as a decision support system (DSS). This work proposed an
encoder-transformer architecture-based framework for multivariate time series
prediction for a prognostics use case. We validated the effectiveness of the
proposed framework on all four sets of the C-MAPPS benchmark dataset for the
remaining useful life prediction task. To effectively transfer the knowledge
and application of transformers from the natural language domain to time
series, three model-specific experiments were conducted. Also, to enable the
model awareness of the initial stages of the machine life and its degradation
path, a novel expanding window method was proposed for the first time in this
work, it was compared with the sliding window method, and it led to a large
improvement in the performance of the encoder transformer model. Finally, the
performance of the proposed encoder-transformer model was evaluated on the test
dataset and compared with the results from 13 other state-of-the-art (SOTA)
models in the literature and it outperformed them all with an average
performance increase of 137.65% over the next best model across all the
datasets
Gap and Overlap Detection in Automated Fiber Placement
The identification and correction of manufacturing defects, particularly gaps
and overlaps, are crucial for ensuring high-quality composite parts produced
through Automated Fiber Placement (AFP). These imperfections are the most
commonly observed issues that can significantly impact the overall quality of
the composite parts. Manual inspection is both time-consuming and
labor-intensive, making it an inefficient approach. To overcome this challenge,
the implementation of an automated defect detection system serves as the
optimal solution. In this paper, we introduce a novel method that uses an
Optical Coherence Tomography (OCT) sensor and computer vision techniques to
detect and locate gaps and overlaps in composite parts. Our approach involves
generating a depth map image of the composite surface that highlights the
elevation of composite tapes (or tows) on the surface. By detecting the
boundaries of each tow, our algorithm can compare consecutive tows and identify
gaps or overlaps that may exist between them. Any gaps or overlaps exceeding a
predefined tolerance threshold are considered manufacturing defects. To
evaluate the performance of our approach, we compare the detected defects with
the ground truth annotated by experts. The results demonstrate a high level of
accuracy and efficiency in gap and overlap segmentation
InsightiGen: a versatile tool to generate insight for an academic systematic literature review
A comprehensive literature review has always been an essential first step of
every meaningful research. In recent years, however, the availability of a vast
amount of information in both open-access and subscription-based literature in
every field has made it difficult, if not impossible, to be certain about the
comprehensiveness of one's survey. This subsequently can lead to reviewers'
questioning of the novelties of the research directions proposed, regardless of
the quality of the actual work presented. In this situation, statistics derived
from the published literature data can provide valuable quantitative and visual
information about research trends, knowledge gaps, and research networks and
hubs in different fields. Our tool provides an automatic and rapid way of
generating insight for systematic reviews in any research area.Comment: 15 pages, 5 figure
Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration
Virtual reality has proved to be useful in applications in several fields
ranging from gaming, medicine, and training to development of interfaces that
enable human-robot collaboration. It empowers designers to explore applications
outside of the constraints posed by the real world environment and develop
innovative solutions and experiences. Hand gestures recognition which has been
a topic of much research and subsequent commercialization in the real world has
been possible because of the creation of large, labelled datasets. In order to
utilize the power of natural and intuitive hand gestures in the virtual domain
for enabling embodied teleoperation of collaborative robots, similarly large
datasets must be created so as to keep the working interface easy to learn and
flexible enough to add more gestures. Depending on the application, this may be
computationally or economically prohibitive. Thus, the adaptation of trained
deep learning models that perform well in the real environment to the virtual
may be a solution to this challenge. This paper presents a systematic framework
for the real to virtual adaptation using limited size of virtual dataset along
with guidelines for creating a curated dataset. Finally, while hand gestures
have been considered as the communication mode, the guidelines and
recommendations presented are generic. These are applicable to other modes such
as body poses and facial expressions which have large datasets available in the
real domain which must be adapted to the virtual one
Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction
UAV-based intelligent data acquisition for 3D reconstruction and monitoring
of infrastructure has been experiencing an increasing surge of interest due to
the recent advancements in image processing and deep learning-based techniques.
View planning is an essential part of this task that dictates the information
capture strategy and heavily impacts the quality of the 3D model generated from
the captured data. Recent methods have used prior knowledge or partial
reconstruction of the target to accomplish view planning for active
reconstruction; the former approach poses a challenge for complex or newly
identified targets while the latter is computationally expensive. In this work,
we present Bag-of-Views (BoV), a fully appearance-based model used to assign
utility to the captured views for both offline dataset refinement and online
next-best-view (NBV) planning applications targeting the task of 3D
reconstruction. With this contribution, we also developed the View Planning
Toolbox (VPT), a lightweight package for training and testing machine
learning-based view planning frameworks, custom view dataset generation of
arbitrary 3D scenes, and 3D reconstruction. Through experiments which pair a
BoV-based reinforcement learning model with VPT, we demonstrate the efficacy of
our model in reducing the number of required views for high-quality
reconstructions in dataset refinement and NBV planning.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L
Model Compression Methods for YOLOv5: A Review
Over the past few years, extensive research has been devoted to enhancing
YOLO object detectors. Since its introduction, eight major versions of YOLO
have been introduced with the purpose of improving its accuracy and efficiency.
While the evident merits of YOLO have yielded to its extensive use in many
areas, deploying it on resource-limited devices poses challenges. To address
this issue, various neural network compression methods have been developed,
which fall under three main categories, namely network pruning, quantization,
and knowledge distillation. The fruitful outcomes of utilizing model
compression methods, such as lowering memory usage and inference time, make
them favorable, if not necessary, for deploying large neural networks on
hardware-constrained edge devices. In this review paper, our focus is on
pruning and quantization due to their comparative modularity. We categorize
them and analyze the practical results of applying those methods to YOLOv5. By
doing so, we identify gaps in adapting pruning and quantization for compressing
YOLOv5, and provide future directions in this area for further exploration.
Among several versions of YOLO, we specifically choose YOLOv5 for its excellent
trade-off between recency and popularity in literature. This is the first
specific review paper that surveys pruning and quantization methods from an
implementation point of view on YOLOv5. Our study is also extendable to newer
versions of YOLO as implementing them on resource-limited devices poses the
same challenges that persist even today. This paper targets those interested in
the practical deployment of model compression methods on YOLOv5, and in
exploring different compression techniques that can be used for subsequent
versions of YOLO.Comment: 18 pages, 7 Figure
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation
Simulation is essential to reinforcement learning (RL) before implementation
in the real world, especially for safety-critical applications like robot
manipulation. Conventionally, RL agents are sensitive to the discrepancies
between the simulation and the real world, known as the sim-to-real gap. The
application of domain randomization, a technique used to fill this gap, is
limited to the imposition of heuristic-randomized models. We investigate the
properties of intrinsic stochasticity of real-time simulation (RT-IS) of
off-the-shelf simulation software and its potential to improve the robustness
of RL methods and the performance of domain randomization. Firstly, we conduct
analytical studies to measure the correlation of RT-IS with the occupation of
the computer hardware and validate its comparability with the natural
stochasticity of a physical robot. Then, we apply the RT-IS feature in the
training of an RL agent. The simulation and physical experiment results verify
the feasibility and applicability of RT-IS to robust RL agent design for robot
manipulation tasks. The RT-IS-powered robust RL agent outperforms conventional
RL agents on robots with modeling uncertainties. It requires fewer heuristic
randomization and achieves better generalizability than the conventional
domain-randomization-powered agents. Our findings provide a new perspective on
the sim-to-real problem in practical applications like robot manipulation
tasks
Integrated Decision Support System for Prognostic and Diagnostic Analyses of Water Distribution System Failures
This paper presents an innovative decision support system (DSS) for prognostic and diagnostic analyses of water distribution system (WDS) failures. The framework of the DSS is based on four novel models developed and published by the authors of this paper. The four models include reliability assessment model, leakage potential model, leakage detection model, and water quality failure potential model. Information obtained from these models together with external information such as customer complaints, lab test results (if any), and historical information are integrated using Dempster-Shafer (D-S) theory to evaluate prognostic and diagnostic capabilities of the DSS. The prognostic capabilities of the DSS provide hydraulic and water quality states of a WDS whereas the diagnostic capabilities of the DSS help to identify the failure location with minimal time after the occurrence and will help to reduce false positive and false negative predictions. The framework has ‘unique’ capacity to bring the modeling information (hydraulic and Quality), consumer complaints, historical failure data, and laboratory test information under a single platform to perform a prognostic and diagnostic investigation of WDS failures (hydraulic and Quality). The proof of concept of the DSS has been demonstrated using data used in published four articles. The outcomes of this research widely addressed the uncertainties associated with WDS which improves the efficiency and effectiveness of diagnosis and prognosis analyses of WDS. It is expected that the developed integrated framework will help municipalities to make informed decisions to increase the safety, reliability and the security of public health.Natural Sciences and Engineering Research Council of Canada (NSERC-SPG (Strategic Project Grants)
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