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Towards an aspect weaving BPEL engine
This position paper proposes the use of dynamic aspects and
the visitor design pattern to obtain a highly configurable and
extensible BPEL engine. Using these two techniques, the
core of this infrastructural software can be customised to
meet new requirements and add features such as debugging,
execution monitoring, or changing to another Web Service
selection policy. Additionally, it can easily be extended to
cope with customer-specific BPEL extensions. We propose
the use of dynamic aspects not only on the engine itself
but also on the workflow in order to tackle the problems of
Web Service hot deployment and hot fixes to long running
processes. In this way, composing aWeb Service "on-the-fly"
means weaving its choreography interface into the workflow
Freeze-drying modeling and monitoring using a new neuro-evolutive technique
This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too
Scalable and Sustainable Deep Learning via Randomized Hashing
Current deep learning architectures are growing larger in order to learn from
complex datasets. These architectures require giant matrix multiplication
operations to train millions of parameters. Conversely, there is another
growing trend to bring deep learning to low-power, embedded devices. The matrix
operations, associated with both training and testing of deep networks, are
very expensive from a computational and energy standpoint. We present a novel
hashing based technique to drastically reduce the amount of computation needed
to train and test deep networks. Our approach combines recent ideas from
adaptive dropouts and randomized hashing for maximum inner product search to
select the nodes with the highest activation efficiently. Our new algorithm for
deep learning reduces the overall computational cost of forward and
back-propagation by operating on significantly fewer (sparse) nodes. As a
consequence, our algorithm uses only 5% of the total multiplications, while
keeping on average within 1% of the accuracy of the original model. A unique
property of the proposed hashing based back-propagation is that the updates are
always sparse. Due to the sparse gradient updates, our algorithm is ideally
suited for asynchronous and parallel training leading to near linear speedup
with increasing number of cores. We demonstrate the scalability and
sustainability (energy efficiency) of our proposed algorithm via rigorous
experimental evaluations on several real datasets
Research Towards High Speed Freeforming
Additive manufacturing (AM) methods are currently utilised for the manufacture of prototypes and low volume, high cost parts. This is because in most cases the high material costs and low volumetric deposition rates of AM parts result in higher per part cost than traditional manufacturing methods. This paper brings together recent research aimed at improving the economics of AM, in particular Extrusion Freeforming (EF).
A new class of machine is described called High Speed Additive Manufacturing (HSAM) in which software, hardware and materials advances are aggregated. HSAM could be cost competitive with injection moulding for medium sized medium quantity parts. A general outline for a HSAM machine and supply chain is provided along with future required research
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Adaptive inferential sensors based on evolving fuzzy models
A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can a- ddress the challenges of the modern advanced process industry
Integrated system to perform surrogate based aerodynamic optimisation for high-lift airfoil
This work deals with the aerodynamics optimisation of a generic two-dimensional three element high-lift configuration. Although the high-lift system is applied only during take-off and landing in the low speed phase of the flight the cost efficiency of the airplane is strongly influenced by it [1]. The ultimate goal of an aircraft high lift system design team is to define the simplest configuration which, for prescribed constraints, will meet the take-off, climb, and landing requirements usually expressed in terms of maximum L/D and/or maximum CL. The ability of the calculation method to accurately predict changes in objective function value when gaps, overlaps and element deflections are varied is therefore critical. Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimisation. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. This work outlines the development of integrated systems to perform aerodynamics multi-objective optimisation for a three-element airfoil test case in high lift configuration, making use of surrogate models available in MACROS Generic Tools, which has been integrated in our design tool. Different metamodeling techniques have been compared based on multiple performance criteria. With MACROS is possible performing either optimisation of the model built with predefined training sample (GSO) or Iterative Surrogate-Based Optimization (SBO). In this first case the model is build independent from the optimisation and then use it as a black box in the optimisation process. In the second case is needed to provide the possibility to call CFD code from the optimisation process, and there is no need to build any model, it is being built internally during the optimisation process. Both approaches have been applied. A detailed analysis of the integrated design system, the methods as well as th
Gating Artificial Neural Network Based Soft Sensor
This work proposes a novel approach to Soft Sensor modelling,
where the Soft Sensor is built by a set of experts which are artificial
neural networks with randomly generated topology. For each of
the experts a meta neural network is trained, the gating Artificial Neural
Network. The role of the gating network is to learn the performance of the
experts in dependency on the input data samples. The final prediction
of the Soft Sensor is a weighted sum of the individual experts predictions.
The proposed meta-learning method is evaluated on two different
process industry data sets
A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters
In this work, we consider the solution of boundary integral equations by
means of a scalable hierarchical matrix approach on clusters equipped with
graphics hardware, i.e. graphics processing units (GPUs). To this end, we
extend our existing single-GPU hierarchical matrix library hmglib such that it
is able to scale on many GPUs and such that it can be coupled to arbitrary
application codes. Using a model GPU implementation of a boundary element
method (BEM) solver, we are able to achieve more than 67 percent relative
parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
with 1.5 million unknowns and a real-world geometry test case with almost 1.2
million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
setup phase and 20 seconds for the iterative solver. To the best of the
authors' knowledge, we here discuss the first fully GPU-based
distributed-memory parallel hierarchical matrix Open Source library using the
traditional H-matrix format and adaptive cross approximation with an
application to BEM problems
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