153 research outputs found
An Optimization Model for Ordering and Transporting Raw Materials by Manufacturers Based on Linear Programming
This study applies analytic hierarchy process (AHP), 0-1 integer programming and other systematic programming models, combined with the needs of manufacturers to build a set of mathematical models to help manufacturers select suppliers, control raw material ordering costs, and transporting costs. As the production costs of products is linked to the ordering and transporting of raw materials, manufacturers need to choose suitable raw material suppliers and develop the most economical ordering and transporting schemes with the most negligible loss so as to improve profits and enhance the market competitiveness. Taking a building and decorative plate enterprise as a case, this model shows that AHP can effectively quantify the supplying characteristics of suppliers and obtain a list of high-quality suppliers. LINGO software combined with linear programming can be used to obtain the optimal weekly ordering and transporting schemes, thus reducing the raw material ordering costs and transporting losses. It is shown from the results that the production costs of enterprises from the source can be reduced so as to improve their cost control system. The present study seeks to fill the gap in ordering and transporting raw materials for manufacturers to control production costs. At the same time, this model provides references for producers who have the demand for processing raw materials and play a significant role in controlling production costs
Quasinormal modes of a scalar perturbation around a rotating BTZ-like black hole in Einstein-bumblebee gravity
We analytically study the quasinormal modes of a scalar perturbation around a
rotating BTZ-like black hole in the Einstein-bumblebee gravity. We observe that
the Lorentz symmetry breaking parameter imprints only in the imaginary parts of
the quasinormal frequencies for the right-moving and left-moving modes. The
perturbational field decays more rapidly for the negative Lorentz symmetry
breaking parameter, but more slowly for the positive one. The forms of the real
parts are the same as those in the usual BTZ black holes. Moreover, we also
discuss the correspondence from the quasinormal modes and find that
the Lorentz symmetry breaking parameter enhances the left and right conformal
weights and of the operators dual to the scalar field in the
boundary. These results could be helpful to understand the
correspondence and the Einstein-bumblebee gravity with the Lorentz symmetry
violation.Comment: 15 pages, 1 figur
Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging
Different medical imaging modalities provide complementary anatomical and
functional information. One increasingly important use of such information is in
the clinical management of cardiovascular disease. Multi-modality data is helping
improve diagnosis accuracy, and individualize treatment. The Clinical Research
Imaging Centre at the University of Edinburgh, has been involved in a number
of cardiovascular clinical trials using longitudinal computed tomography (CT) and
multi-parametric magnetic resonance (MR) imaging. The critical image processing
technique that combines the information from all these different datasets is known
as image registration, which is the topic of this thesis. Image registration, especially
multi-modality and multi-parametric registration, remains a challenging field in
medical image analysis. The new registration methods described in this work were
all developed in response to genuine challenges in on-going clinical studies. These
methods have been evaluated using data from these studies.
In order to gain an insight into the building blocks of image registration methods,
the thesis begins with a comprehensive literature review of state-of-the-art algorithms.
This is followed by a description of the first registration method I developed to help
track inflammation in aortic abdominal aneurysms. It registers multi-modality and
multi-parametric images, with new contrast agents. The registration framework uses a
semi-automatically generated region of interest around the aorta. The aorta is aligned
based on a combination of the centres of the regions of interest and intensity matching.
The method achieved sub-voxel accuracy.
The second clinical study involved cardiac data. The first framework failed to
register many of these datasets, because the cardiac data suffers from a common
artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I
developed a new preprocessing technique that is able to correct the artefacts in the
functional data using data from the anatomical scans. The registration framework,
with this preprocessing step and new particle swarm optimizer, achieved significantly
improved registration results on the cardiac data, and was validated quantitatively
using neuro images from a clinical study of neonates. Although on average
the new framework achieved accurate results, when processing data corrupted
by severe artefacts and noise, premature convergence of the optimizer is still a
common problem. To overcome this, I invented a new optimization method, that
achieves more robust convergence by encoding prior knowledge of registration. The
registration results from this new registration-oriented optimizer are more accurate
than other general-purpose particle swarm optimization methods commonly applied
to registration problems.
In summary, this thesis describes a series of novel developments to an image
registration framework, aimed to improve accuracy, robustness and speed. The
resulting registration framework was applied to, and validated by, different types of
images taken from several ongoing clinical trials. In the future, this framework could
be extended to include more diverse transformation models, aided by new machine
learning techniques. It may also be applied to the registration of other types and
modalities of imaging data
Learning to synthesise the ageing brain without longitudinal data
How will my face look when I get older? Or, for a more challenging question:
How will my brain look when I get older? To answer this question one must
devise (and learn from data) a multivariate auto-regressive function which
given an image and a desired target age generates an output image. While
collecting data for faces may be easier, collecting longitudinal brain data is
not trivial. We propose a deep learning-based method that learns to simulate
subject-specific brain ageing trajectories without relying on longitudinal
data. Our method synthesises images conditioned on two factors: age (a
continuous variable), and status of Alzheimer's Disease (AD, an ordinal
variable). With an adversarial formulation we learn the joint distribution of
brain appearance, age and AD status, and define reconstruction losses to
address the challenging problem of preserving subject identity. We compare with
several benchmarks using two widely used datasets. We evaluate the quality and
realism of synthesised images using ground-truth longitudinal data and a
pre-trained age predictor. We show that, despite the use of cross-sectional
data, our model learns patterns of gray matter atrophy in the middle temporal
gyrus in patients with AD. To demonstrate generalisation ability, we train on
one dataset and evaluate predictions on the other. In conclusion, our model
shows an ability to separate age, disease influence and anatomy using only 2D
cross-sectional data that should be useful in large studies into
neurodegenerative disease, that aim to combine several data sources. To
facilitate such future studies by the community at large our code is made
available at https://github.com/xiat0616/BrainAgeing
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT
In modern Internet of Things (IoT), visual analysis and predictions are often performed by deep learning models. Salient object detection (SOD) is a fundamental preprocessing for these applications. Executing SOD on the fog devices is a challenging task due to the diversity of data and fog devices. To adopt convolutional neural networks (CNN) on fog-cloud infrastructures for SOD-based applications, we introduce a semisupervised adversarial learning method in this article. The proposed model, named as SaliencyGAN, is empowered by a novel concatenated generative adversarial network (GAN) framework with partially shared parameters. The backbone CNN can be chosen flexibly based on the specific devices and applications. In the meanwhile, our method uses both the labeled and unlabeled data from different problem domains for training. Using multiple popular benchmark datasets, we compared state-of-the-art baseline methods to our SaliencyGAN obtained with 10-100% labeled training data. SaliencyGAN gained performance comparable to the supervised baselines when the percentage of labeled data reached 30%, and outperformed the weakly supervised and unsupervised baselines. Furthermore, our ablation study shows that SaliencyGAN were more robust to the common “mode missing” (or “mode collapse”) issue compared to the selected popular GAN models. The visualized ablation results have proved that SaliencyGAN learned a better estimation of data distributions. To the best of our knowledge, this is the first IoT-oriented semisupervised SOD method
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