237 research outputs found
Material Measurement Units: Foundations Through a Survey
Long-term availability of minerals and industrial materials is a necessary
condition for sustainable development as they are the constituents of any
manufacturing product. In particular, technologies with increasing demand such
as GPUs and photovoltaic panels are made of critical raw materials. To enhance
the efficiency of material management, in this paper we make three main
contributions: first, we identify in the literature an emerging
computer-vision-enabled material monitoring technology which we call Material
Measurement Unit (MMU); second, we provide a survey of works relevant to the
development of MMUs; third, we describe a material stock monitoring sensor
network deploying multiple MMUs.Comment: In preparation for submission to ACM Computing Survey
An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in vision-based Human-Robot Collaboration
To enable safe and effective human-robot collaboration (HRC) in smart
manufacturing, seamless integration of sensing, cognition, and prediction into
the robot controller is critical for real-time awareness, response, and
communication inside a heterogeneous environment (robots, humans, and
equipment). The proposed approach takes advantage of the prediction
capabilities of nonlinear model predictive control (NMPC) to execute a safe
path planning based on feedback from a vision system. In order to satisfy the
requirement of real-time path planning, an embedded solver based on a penalty
method is applied. However, due to tight sampling times NMPC solutions are
approximate, and hence the safety of the system cannot be guaranteed. To
address this we formulate a novel safety-critical paradigm with an exponential
control barrier function (ECBF) used as a safety filter. We also design a
simple human-robot collaboration scenario using V-REP to evaluate the
performance of the proposed controller and investigate whether integrating
human pose prediction can help with safe and efficient collaboration. The robot
uses OptiTrack cameras for perception and dynamically generates collision-free
trajectories to the predicted target interactive position. Results for a number
of different configurations confirm the efficiency of the proposed motion
planning and execution framework. It yields a 19.8% reduction in execution time
for the HRC task considered
Design and flux-weakening control of an interior permanent magnet synchronous motor for electric vehicles
Permanent magnet synchronous motors (PMSMs) provide a competitive technology for EV traction drives owing to their high power density and high efficiency. In this paper, three types of interior PMSMs with different PM arrangements are modeled by the finite element method (FEM). For a given amount of permanent magnet materials, the V shape interior PMSM is found better than the U-shape and the conventional rotor topologies for EV traction drives. Then the V shape interior PMSM is further analyzed with the effects of stator slot opening and the permanent magnet pole chamfering on cogging torque and output torque performance. A vector-controlled flux-weakening method is developed and simulated in matlab to expand the motor speed range for EV drive system. The results show good dynamic and steady-state performance with a capability of expanding speed up to 4 times of the rated. A prototype of the V shape interior PMSM is also manufactured and tested to validate the numerical models built by the finite element method
Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks
Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OES) data from the endpoint detection system. Simulation studies demonstrate the feasibility and the effectiveness of the proposed approach
Greetings from the conference chairs
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record
Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study
Dimensionality reduction is a important step in the development of scalable
and interpretable data-driven models, especially when there are a large number
of candidate variables. This paper focuses on unsupervised variable selection
based dimensionality reduction, and in particular on unsupervised greedy
selection methods, which have been proposed by various researchers as
computationally tractable approximations to optimal subset selection. These
methods are largely distinguished from each other by the selection criterion
adopted, which include squared correlation, variance explained, mutual
information and frame potential. Motivated by the absence in the literature of
a systematic comparison of these different methods, we present a critical
evaluation of seven unsupervised greedy variable selection algorithms
considering both simulated and real world case studies. We also review the
theoretical results that provide performance guarantees and enable efficient
implementations for certain classes of greedy selection function, related to
the concept of submodularity. Furthermore, we introduce and evaluate for the
first time, a lazy implementation of the variance explained based forward
selection component analysis (FSCA) algorithm. Our experimental results show
that: (1) variance explained and mutual information based selection methods
yield smaller approximation errors than frame potential; (2) the lazy FSCA
implementation has similar performance to FSCA, while being an order of
magnitude faster to compute, making it the algorithm of choice for unsupervised
variable selection.Comment: Submitted to Engineering Applications of Artificial Intelligenc
Intervento del rappresentante degli studenti, dott.ssa Gisella De Rosa
Research presented in this paper was funded by a Strategic Research Cluster grant [07/SRC/I1168] by the Science Foundation Ireland under the National Development Plan. Special Issue: Web and wireless GISThe quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.PostprintPeer reviewe
Adaptive Safety-critical Control with Uncertainty Estimation for Human-robot Collaboration
In advanced manufacturing, strict safety guarantees are required to allow
humans and robots to work together in a shared workspace. One of the challenges
in this application field is the variety and unpredictability of human
behavior, leading to potential dangers for human coworkers. This paper presents
a novel control framework by adopting safety-critical control and uncertainty
estimation for human-robot collaboration. Additionally, to select the shortest
path during collaboration, a novel quadratic penalty method is presented. The
innovation of the proposed approach is that the proposed controller will
prevent the robot from violating any safety constraints even in cases where
humans move accidentally in a collaboration task. This is implemented by the
combination of a time-varying integral barrier Lyapunov function (TVIBLF) and
an adaptive exponential control barrier function (AECBF) to achieve a flexible
mode switch between path tracking and collision avoidance with guaranteed
closed-loop system stability. The performance of our approach is demonstrated
in simulation studies on a 7-DOF robot manipulator. Additionally, a comparison
between the tasks involving static and dynamic targets is provided
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