1,995 research outputs found
Preliminary specification and design documentation for software components to achieve catallaxy in computational systems
This Report is about the preliminary specifications and design documentation for software components to achieve Catallaxy in computational systems. -- Die Arbeit beschreibt die Spezifikation und das Design von Softwarekomponenten, um das Konzept der Katallaxie in Grid Systemen umzusetzen. Eine Einführung ordnet das Konzept der Katallaxie in bestehende Grid Taxonomien ein und stellt grundlegende Komponenten vor. Anschließend werden diese Komponenten auf ihre Anwendbarkeit in bestehenden Application Layer Netzwerken untersucht.Grid Computing
Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics
Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy
Dynamic Programming and Bayesian Inference
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming
On Practical machine Learning and Data Analysis
This thesis discusses and addresses some of the difficulties
associated with practical machine learning and data
analysis. Introducing data driven methods in e.g industrial and
business applications can lead to large gains in productivity and
efficiency, but the cost and complexity are often
overwhelming. Creating machine learning applications in practise often
involves a large amount of manual labour, which often needs to be
performed by an experienced analyst without significant experience
with the application area. We will here discuss some of the hurdles
faced in a typical analysis project and suggest measures and methods
to simplify the process.
One of the most important issues when applying machine learning
methods to complex data, such as e.g. industrial applications, is that
the processes generating the data are modelled in an appropriate
way. Relevant aspects have to be formalised and represented in a way
that allow us to perform our calculations in an efficient manner. We
present a statistical modelling framework, Hierarchical Graph
Mixtures, based on a combination of graphical models and mixture
models. It allows us to create consistent, expressive statistical
models that simplify the modelling of complex systems. Using a
Bayesian approach, we allow for encoding of prior knowledge and make
the models applicable in situations when relatively little data are
available.
Detecting structures in data, such as clusters and dependency
structure, is very important both for understanding an application
area and for specifying the structure of e.g. a hierarchical graph
mixture. We will discuss how this structure can be extracted for
sequential data. By using the inherent dependency structure of
sequential data we construct an information theoretical measure of
correlation that does not suffer from the problems most common
correlation measures have with this type of data.
In many diagnosis situations it is desirable to perform a
classification in an iterative and interactive manner. The matter is
often complicated by very limited amounts of knowledge and examples
when a new system to be diagnosed is initially brought into use. We
describe how to create an incremental classification system based on a
statistical model that is trained from empirical data, and show how
the limited available background information can still be used
initially for a functioning diagnosis system.
To minimise the effort with which results are achieved within data
analysis projects, we need to address not only the models used, but
also the methodology and applications that can help simplify the
process. We present a methodology for data preparation and a software
library intended for rapid analysis, prototyping, and deployment.
Finally, we will study a few example applications, presenting tasks
within classification, prediction and anomaly detection. The examples
include demand prediction for supply chain management, approximating
complex simulators for increased speed in parameter optimisation, and
fraud detection and classification within a media-on-demand system
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Context: Machine Learning (ML) has been at the heart of many innovations over
the past years. However, including it in so-called 'safety-critical' systems
such as automotive or aeronautic has proven to be very challenging, since the
shift in paradigm that ML brings completely changes traditional certification
approaches.
Objective: This paper aims to elucidate challenges related to the
certification of ML-based safety-critical systems, as well as the solutions
that are proposed in the literature to tackle them, answering the question 'How
to Certify Machine Learning Based Safety-critical Systems?'.
Method: We conduct a Systematic Literature Review (SLR) of research papers
published between 2015 to 2020, covering topics related to the certification of
ML systems. In total, we identified 217 papers covering topics considered to be
the main pillars of ML certification: Robustness, Uncertainty, Explainability,
Verification, Safe Reinforcement Learning, and Direct Certification. We
analyzed the main trends and problems of each sub-field and provided summaries
of the papers extracted.
Results: The SLR results highlighted the enthusiasm of the community for this
subject, as well as the lack of diversity in terms of datasets and type of
models. It also emphasized the need to further develop connections between
academia and industries to deepen the domain study. Finally, it also
illustrated the necessity to build connections between the above mention main
pillars that are for now mainly studied separately.
Conclusion: We highlighted current efforts deployed to enable the
certification of ML based software systems, and discuss some future research
directions.Comment: 60 pages (92 pages with references and complements), submitted to a
journal (Automated Software Engineering). Changes: Emphasizing difference
traditional software engineering / ML approach. Adding Related Works, Threats
to Validity and Complementary Materials. Adding a table listing papers
reference for each section/subsection
Modelling discrepancy in Bayesian calibration of reservoir models
Simulation models of physical systems such as oil field reservoirs are subject to numerous uncertainties such as observation errors and inaccurate initial and boundary conditions. However, after accounting for these uncertainties, it is usually observed that the mismatch between the simulator output and the observations remains and the model is still inadequate. This incapability of computer models to reproduce the real-life processes is referred to as model inadequacy.
This thesis presents a comprehensive framework for modelling discrepancy in the Bayesian calibration and probabilistic forecasting of reservoir models. The framework efficiently implements data-driven approaches to handle uncertainty caused by ignoring the modelling discrepancy in reservoir predictions using two major hierarchical strategies, parametric and non-parametric hierarchical models.
The central focus of this thesis is on an appropriate way of modelling discrepancy and the importance of the model selection in controlling overfitting rather than different solutions to different noise models.
The thesis employs a model selection code to obtain the best candidate solutions to the form of non-parametric error models. This enables us to, first, interpolate the error in history period and, second, propagate it towards unseen data (i.e. error generalisation). The error models constructed by inferring parameters of selected models can predict the response variable (e.g. oil rate) at any point in input space (e.g. time) with corresponding generalisation uncertainty.
In the real field applications, the error models reliably track down the uncertainty regardless of the type of the sampling method and achieve a better model prediction score compared to the models that ignore discrepancy.
All the case studies confirm the enhancement of field variables prediction when the discrepancy is modelled. As for the model parameters, hierarchical error models render less global bias concerning the reference case. However, in the considered case studies, the evidence for better prediction of each of the model parameters by error modelling is inconclusive
RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques
Construction waste disposal is an urgent issue
for protecting our environment. This paper proposes a
waste management system and illustrates the work
process using plasterboard waste as an example, which
creates a hazardous gas when land filled with household
waste, and for which the recycling rate is less than 10%
in the UK. The proposed system integrates RFID
technology, Rule-Based Reasoning, Ant Colony
optimization and knowledge technology for auditing
and tracking plasterboard waste, guiding the operation
staff, arranging vehicles, schedule planning, and also
provides evidence to verify its disposal. It h relies on
RFID equipment for collecting logistical data and uses
digital imaging equipment to give further evidence; the
reasoning core in the third layer is responsible for
generating schedules and route plans and guidance, and
the last layer delivers the result to inform users. The
paper firstly introduces the current plasterboard
disposal situation and addresses the logistical problem
that is now the main barrier to a higher recycling rate,
followed by discussion of the proposed system in terms
of both system level structure and process structure.
And finally, an example scenario will be given to
illustrate the system’s utilization
A Strategic Digital Transformation for the Water Industry
This book is a compilation of the knowledge shared and generated so far in the IWA Digital Water Programme. It is an insightful collection of white papers covering best practices, linking academic and industrial studies/insights with applications to give real-world examples of digital transformation. These White Papers are designed to help utilities, water professionals and all those interested in water management and stewardship issues to better understand the opportunities of digital technologies.
This book covers a plethora of topics including:
Instrumentation and data generation
Artificial intelligence and digital twins
The digital transformation and public health
Mapping the digital transformation journey into the future
With these topics, the aim is to present an all-encompassing reference for practitioners to use in their day-to-day activities. Through the Digital Water Programme, the IWA leverages its worldwide member expertise to guide a new generation of water and wastewater utilities on their digital journey towards the uptake of digital technologies and their integration into water services
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