9,409 research outputs found
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Investigation of Inorganic Salt Hydrate Phase Change Materials for Thermal Energy Storage Integrated into Heat Pump Systems
Thermal energy storage (TES) is a promising technology for the Energy Transition. Low grade heat is valuable for many everyday applications: indoor heating and cooling, hot water, refrigeration, etc. Heat pumps (HPs) move heat up a thermal gradient (from cold to hot) with an input of energy. Integrating TES into a HP grants flexibility to dispatch the stored heat as needed. When operating a HP against a fluctuating temperature body (i.e., outdoor ambient air temperature), TES provides an isothermal heat source that enables more efficient HP operation to its reduce energy consumption without sacrificing thermal comfort. This work explores the thermodynamic limits of HP-TES and it was found that TES temperatures equal to the application temperature leads to the highest potential for energy savings and peak demand reduction. This HP-TES system was then modeled in a building thermal energy simulation where the same findings emerge: a TES temperature near the application temperature shows the highest potential. A common method of achieving an isothermal TES is to incorporate phase change materials (PCMs) that store heat through the enthalpy of phase change. Salt hydrates are valued for their high volumetric storage density and low cost. This work explores the Brunauer-Emmett-Teller method to model sodium sulfate, but this salt was found to be incompatible with this reduced order method. Salt hydrates also exhibit low thermal conductivity which limits their direct use in TES. This work develops salt hydrate-graphite composite PCMs with improved thermal conductivity, however a tradeoff between energy storage capacity and thermal power density was seen. The composite PCMs were experimentally tested in a TES device and the improved thermal properties demonstrate their potential for use in simple TES architectures. Overall, this work evaluated TES systems from a holistic perspective, spanning several orders of magnitude, both energetically and spatially.Ph.D
Geometric Error Identification for 6DoF Robotic Manipulator Calibration to Improve Absolute Positioning Accuracy
As robotic manipulators become extensively incorporated in various modern industries, there is a growing list of applications for human-to-robot interaction and robot-to-robot collaboration, which requires strong performance on the absolute positioning accuracy of the robot. The lack of accuracy could come from extreme operating environments, manufacturing and assembly errors, dynamic influence from gear compliance and backlash, etc. This thesis tackles the accuracy issue from two aspects: tighter mechanical tolerances and a closer matching kinematics model with the actual robot. For these purposes, according to the geometry of a pneumatically driven six DoF manipulator, a 6D parametric kinematics model is firstly derived. The proposed model is highly flexible in terms of introducing, anywhere in each linkage of the manipulator, any number of virtual mechanical tolerance points that lump effects of dimension and orientation deviations caused by mechanical tolerances. Therefore, concerned mechanical tolerances can be added to the model and studied through Fuzzy arithmetic to analyze their influence on the TCP position. Meanwhile, geometric errors are also the primary source of discrepancies between the nominal model and real hardware. The model can include translational and rotational error parameters that need identification to quantify the effects from the geometric errors at the locations of the virtual mechanical tolerance points. For an effective identification, dependent error parameters are systematically eliminated using QR decomposition. Once the model reduction is completed, the nonlinear least-squares optimization problem using the Gauss-Newton line search method is formulated to identify the remaining independent error parameters. The identification process is eventually verified on the experimental manipulator. In a nutshell, the thesis presents 1) a tolerance analysis tool that offers insights for potential targeted manufacturing improvements to decrease the dominant tolerances, and 2) a capable parameter identification process that rectifies the nominal kinematics model to agree with the hardware.M.S
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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Machine Learning for Gravitational-Wave Astronomy: Methods and Applications for High-Dimensional Laser Interferometry Data
Gravitational-wave astronomy is an emerging field in observational astrophysics concerned with the study of gravitational signals proposed to exist nearly a century ago by Albert Einstein but only recently confirmed to exist. Such signals were theorized to result from astronomical events such as the collisions of black holes, but they were long thought to be too faint to measure on Earth. In recent years, the construction of extremely sensitive detectors—including the Laser Interferometer Gravitational-Wave Observatory (LIGO) project—has enabled the first direct detections of these gravitational waves, corroborating the theory of general relativity and heralding a new era of astrophysics research.
As a result of their extraordinary sensitivity, the instruments used to study gravitational waves are also subject to noise that can significantly limit their ability to detect the signals of interest with sufficient confidence. The detectors continuously record more than 200,000 time series of auxiliary data describing the state of a vast array of internal components and sensors, the environmental state in and around the detector, and so on. This data offers significant value for understanding the nearly innumerable potential sources of noise and ultimately reducing or eliminating them, but it is clearly impossible to monitor, let alone understand, so much information manually. The field of machine learning offers a variety of techniques well-suited to problems of this nature.
In this thesis, we develop and present several machine learning–based approaches to automate the process of extracting insights from the vast, complex collection of data recorded by LIGO detectors. We introduce a novel problem formulation for transient noise detection and show for the first time how an efficient and interpretable machine learning method can accurately identify detector noise using all of these auxiliary data channels but without observing the noise itself. We present further work employing more sophisticated neural network–based models, demonstrating how they can reduce error rates by over 60% while also providing LIGO scientists with interpretable insights into the detector’s behavior. We also illustrate the methods’ utility by demonstrating their application to a specific, recurring type of transient noise; we show how we can achieve a classification accuracy of over 97% while also independently corroborating the results of previous manual investigations into the origins of this type of noise.
The methods and results presented in the following chapters are applicable not only to the specific gravitational-wave data considered but also to a broader family of machine learning problems involving prediction from similarly complex, high-dimensional data containing only a few relevant components in a sea of irrelevant information. We hope this work proves useful to astrophysicists and other machine learning practitioners seeking to better understand gravitational waves, extremely complex and precise engineered systems, or any of the innumerable extraordinary phenomena of our civilization and universe
Design and validation of a destination management model for the reconstruction of tourism in Manabí (Ecuador) after the 2016 earthquake
La presente investigación aborda la implementación de un plan de gestión post-crisis de destino turístico para la provincia de Manabí tras el terremoto del 16 de abril del 2016. Partiendo del estado de la cuestión y las aportaciones tanto teóricas como prácticas sobre tendencias en la gestión postcrisis de destinos turísticos, así como de la revisión de los estudios de caso sobre destinos turísticos que han sufrido las consecuencias de alguna catástrofe socio-natural, se ha realizado una evaluación de las políticas públicas con incidencia en la zona en la etapa post-crisis y del papel y opiniones de los agentes públicos y privados implicados, todo ello para aportar finalmente un diseño metodológico de selección de planes y herramientas que contribuyan a la reconstrucción y revalorización de los atractivos turísticos de la provincia, así como la para la rentabilización de los recursos materiales y humanos disponibles para superar la crisis. Se discuten finalmente las diferentes estrategias aplicadas en destinos similares, derivándose de ello una serie de reflexiones y conclusiones sobre el modelo y diseño de plan que puede beneficiar la reconstrucción del destino y mejorar sus niveles de calidad y posicionamiento a nivel nacional e internacional
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
Recent Advances in Single-Particle Tracking: Experiment and Analysis
This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion
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