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Assessment of paint appearance quality in the automotive industry
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.In the modem automotive industry, more and more manufacturers recognise that
vehicle paint appearance makes an important contribution to customer satisfaction.
Attractive appearance has become one of the important factors for customers in
making a decision to purchase a car. Objective measurement of the quality of
autobody paint appearance, as perceived by the customer, in a repeatable,
reproducible, continuous scale manner is an important requirement for improving the
paint appearance. It can provide car manufacturers a standard reference to evaluate
the quality of the paint appearance.
This thesis mainly deals with the measurement of paint appearance quality in the
automotive industry by investigating, identifying and developing measurement methods in
this area. First of all, the 'state of the art' in the area of paint appearance measurement
was presented, which summarised the concept of appearance, models, attributes and
definitions. To further identify the parameters and instruments used in the automotive
industry, a round robin test was launched to perform visual assessment and instrument
measurements on a set of panels in some European car manufacturers. A summary of
the correlation found between measurable parameters and visual assessment provided
the basis of the further work. Based on the literature survey and round robin test
results, the next work is mainly concentrated on the two most important parameters,
'orange peel' and 'metal texture effect', how to separate and evaluate them. Digital
signal processing technique, FFT and Filtering, have been employed to separate them
and a set of measures have been provided for evaluation. At the same time, the
technique for texture pattern recognition was introduced to evaluate the texture effect
when a fine texture comparison was needed. A set of computable textural parameters
based on grey-tone spatial-dependence matrices gives good correlation directly
corresponding to visual perception.
To resolve the overall appearance modelling problem, two novel and more powerful
modelling tools, artificial neural networks and fuzzy logic, are introduced to model the
overall appearance. The test results showed that both of them are able to reflect the
correlation between overall appearance and the major parameters measured from a
painted surface.
Finally, an integrated measurement system, 'Smart Appearance', was developed using
the image processing techniques and the artificial neural network model. The
implement results show that this system can measure the major attributes of paint
appearance and provide an overall appearance index corresponding to human visual
perception. This system is helpful to product quality control on car body paint. It also
could be used on the paint production line for dynamic measurement.European Union BRITE EURAM Projec
An Algorithmic Framework for Efficient Large-Scale Circuit Simulation Using Exponential Integrators
We propose an efficient algorithmic framework for time domain circuit
simulation using exponential integrator. This work addresses several critical
issues exposed by previous matrix exponential based circuit simulation
research, and makes it capable of simulating stiff nonlinear circuit system at
a large scale. In this framework, the system's nonlinearity is treated with
exponential Rosenbrock-Euler formulation. The matrix exponential and vector
product is computed using invert Krylov subspace method. Our proposed method
has several distinguished advantages over conventional formulations (e.g., the
well-known backward Euler with Newton-Raphson method). The matrix factorization
is performed only for the conductance/resistance matrix G, without being
performed for the combinations of the capacitance/inductance matrix C and
matrix G, which are used in traditional implicit formulations. Furthermore, due
to the explicit nature of our formulation, we do not need to repeat LU
decompositions when adjusting the length of time steps for error controls. Our
algorithm is better suited to solving tightly coupled post-layout circuits in
the pursuit for full-chip simulation. Our experimental results validate the
advantages of our framework.Comment: 6 pages; ACM/IEEE DAC 201
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
In this work, we decouple the iterative bi-level offline RL (value estimation
and policy extraction) from the offline training phase, forming a non-iterative
bi-level paradigm and avoiding the iterative error propagation over two levels.
Specifically, this non-iterative paradigm allows us to conduct inner-level
optimization (value estimation) in training, while performing outer-level
optimization (policy extraction) in testing. Naturally, such a paradigm raises
three core questions that are not fully answered by prior non-iterative offline
RL counterparts like reward-conditioned policy: (q1) What information should we
transfer from the inner-level to the outer-level? (q2) What should we pay
attention to when exploiting the transferred information for safe/confident
outer-level optimization? (q3) What are the benefits of concurrently conducting
outer-level optimization during testing? Motivated by model-based optimization
(MBO), we propose DROP (design from policies), which fully answers the above
questions. Specifically, in the inner-level, DROP decomposes offline data into
multiple subsets, and learns an MBO score model (a1). To keep safe exploitation
to the score model in the outer-level, we explicitly learn a behavior embedding
and introduce a conservative regularization (a2). During testing, we show that
DROP permits deployment adaptation, enabling an adaptive inference across
states (a3). Empirically, we evaluate DROP on various tasks, showing that DROP
gains comparable or better performance compared to prior methods.Comment: NeurIPS 202
Dynamic analysis of Th1/Th2 cytokine concentration during antiretroviral therapy of HIV-1/HCV co-infected Patients
<p>Abstract</p> <p>Background</p> <p>Co-infection with hepatitis C (HCV) is very common in human immunodeficiency virus 1 (HIV-1) infected patients. Although HIV co-infection clearly accelerates progression of HCV-related fibrosis and liver disease, controversy remains as to the impact of HCV on HIV disease progression in co-infected patients. HIV can cause immune dysfunction, in which the regulatory function of T helper (Th) cells is very essential. Moreover, cytokines derived from Th cells play a prominent role in viral infection. Investigating the functional changes of Th1 and Th2 cells in cytokine level can improve the understanding of the effect of co-infected HCV on HIV infection.</p> <p>Methods</p> <p>In this study, we measured the baseline Th1/Th2 cytokine concentration in sera by using flow cytometry in HIV/HCV co-infection, HIV mono-infection, HCV mono-infection, and healthy control group, as well as the dynamic changes of these cytokine levels after receiving highly active antiretroviral therapy (HAART).</p> <p>Results</p> <p>The ratio of Th1 and Th2 cytokine concentration in HIV/HCV co-infection was higher than HCV mono-infection and healthy control group, while lower than HIV mono-infection group. After HAART was initiated, the Th1/Th2 ratio of HIV/HCV co-infection group decreased to the same level of healthy control, while HIV mono-infection group was still higher than the control group.</p> <p>Conclusions</p> <p>There was no significant evidence showing co-infected with HCV had negative effect on HIV related diseases. However, co-infected with HCV can decrease Th1/Th2 ratio by affecting Th1 cytokine level, especially the secretion of IFN-γ. With the initiation of HAART, Th1 and Th2 cytokine levels were progressively reduced. HIV was the main stimulating factor of T cells in HIV/HCV co-infection group.</p
CEIL: Generalized Contextual Imitation Learning
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation
\textbf{L}earning~(CEIL), a general and broadly applicable algorithm for
imitation learning (IL). Inspired by the formulation of hindsight information
matching, we derive CEIL by explicitly learning a hindsight embedding function
together with a contextual policy using the hindsight embeddings. To achieve
the expert matching objective for IL, we advocate for optimizing a contextual
variable such that it biases the contextual policy towards mimicking expert
behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL
is a generalist that can be effectively applied to multiple settings including:
1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL
(mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate
CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline).
Compared to prior state-of-the-art baselines, we show that CEIL is more
sample-efficient in most online IL tasks and achieves better or competitive
performances in offline tasks.Comment: NeurIPS 202
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