Automatic generation of software applications: a platform-based MDA approach

The Model Driven Architecture (MDA) allows moving the software development from the time consuming and error-prone level of writing program code to the next higher level of modeling. In order to gain benefit from this innovative technology, it is necessary to satisfy two requirements. These are first, the creation of compact, complete and correct platform independent models (PIM) and second, the development of a flexible and extensible model transformation framework taking into account frequent changes of the target platform. In this thesis a platform-based methodology is developed to create PIM by abstracting common modeling elements into a platform independent modeling library called Design Platform Model (DPM). The DPM contains OCL-based types for modeling primitive and collection types, a platform independent GUI toolkit as well as other common modeling elements, such as those for IO-operations. Furthermore, a DPM profile containing diverse domain specific and design pattern-based stereotypes is also developed to create PIM with high-level semantics. The behavior in PIM is specified using an OCL-like action language called eXecutable OCL (XOCL), which is also developed in this thesis. For model transformation, the model compiler MOCCA is developed based on a flexible and extensible architecture. The model mapper components in the current version of MOCCA are able to map desktop applications onto JSE platform; the both business object layer and persistence layer of a three-layered enterprise applications onto JEE platform and SAP ABAP platform. The entire model transformation process is finished with complete code generation

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Transport properties and melt distribution of partially molten mantle rocks: insights from micro-computed tomography and virtual rock physics simulations

Mid-ocean ridges are a fundamental component of plate tectonics on Earth. They are the longest mountain ranges; combined, they stretch over 70,000 km of the Earth’s surface. They are significant sources of volcanism, producing more than 20 km3 of new oceanic crust each year. The volcanism observed at the ridge axis is linked to processes that transport and focus melt in the underlying upper mantle. Typically, upper mantle melt distribution is inferred either through inversion of geophysical data, such as electromagnetic signals, or through geodynamic modeling. Both approaches require robust constitutive relationship between on electrical conductivity, permeability, and porosity. Unfortunately, direct measurements of transport properties of partially molten rock are technically challenging due to the extreme conditions required for melting. This work aims to quantify permeability-porosity and electrical conductivity-porosity relationships of partially molten monomineralic and polymineralic aggregates by simulating fluid flow and direct current within experimentally obtained, high-resolution, three-dimensional (3-D) microstructures of partially molten rocks. In this study, I synthesized rocks containing various proportions of olivine, orthopyroxene (opx), and basaltic melt, common components of the upper mantle. I imaged their 3-D microstructure using high-resolution, synchrotron-based X-ray micro-computed tomography. The resulting 3-D geometries constitute virtual rock samples on which pore morphology, permeability, and electrical conductivity were numerically quantified. This work yields microstructure-based electrical conductivity-porosity and permeability-porosity power laws for olivine-melt and olivine-opx-melt aggregates containing melt fractions of 0.02 to 0.20. By directly comparing the velocity and electrical fields, which are outputs of the fluid flow and direct current simulations, respectively, this study provides strong evidence that fluid and electricity travel through distinctly different pathways within the same rock, due to the stronger dependence of fluid flux on hydraulic radius. This study also provides the first quantitative evidence of lithological melt partitioning, where melt fractions spatially associated with olivine are systematically higher than those with orthopyroxene due to the relatively low surface energy density of olivine-melt interfaces with respect to opx-melt interfaces. The results of this study place important, novel constraints on 3-D melt distribution and transport properties of the partially molten mantle regions beneath mid-ocean ridges

Network Resource and Performance Optimization in Autonomous Systems: A Connected Vehicles and Autonomous Networks Perspective

This thesis covers two topics that optimize a network-related problem subject to environment-specific constraints; placing vehicular applications and executing network traffic assignment changes. The first topic introduces an optimization model, Resource and Delay-aware V2X service Placement (RDP), and a baseline approach that only considers the resource requirements of vehicular services. Both are responsible for placing vehicular services used by vehicular applications in an edge computing environment. Under different simulation scenarios, the results obtained by RDP satisfy the delay requirements of vehicular applications as opposed to the baseline approach. The second topic examines the efficient execution of inter-domain traffic changes under bandwidth, monetary, and infrastructural constraints. An oracle algorithm and two heuristics are formulated, and evaluation criteria are devised to reflect the constraints. These algorithms are evaluated on different networks, and the results reported show that OrderSteps (OSS) heuristic satisfies the constraints and outperforms the oracle implementation in terms of run-time

Transport Systems: Safety Modeling, Visions and Strategies

This reprint includes papers describing the synthesis of current theory and practice of planning, design, operation, and safety of modern transport, with special focus on future visions and strategies of transport sustainability, which will be of interest to scientists dealing with transport problems and generally involved in traffic engineering as well as design, traffic networks, and maintenance engineers

Geophysics for Mineral Exploration

This Special Issue contains ten papers which focus on emerging geophysical techniques for mineral exploration, novel modeling, and interpretation methods, including joint inversions of multi physics data, and challenging case studies. The papers cover a wide range of mineral deposits, including banded iron formations, epithermal gold–silver–copper–iron–molybdenum deposits, iron-oxide–copper–gold deposits, and prospecting forgroundwater resources

Deep Visual Feature Learning for Vehicle Detection, Recognition and Re-identification

Along with the ever-increasing number of motor vehicles in current transportation systems, intelligent video surveillance and management becomes more necessary which is one of the important artificial intelligence fields. Vehicle-related problems are being widely explored and applied practically. Among various techniques, computer vision and machine learning algorithms have been the most popular ones since a vast of video/image surveillance data are available for research, nowadays. In this thesis, vision-based approaches for vehicle detection, recognition, and re-identification are extensively investigated. Moreover, to address different challenges, several novel methods are proposed to overcome weaknesses of previous works and achieve compelling performance. Deep visual feature learning has been widely researched in the past five years and obtained huge progress in many applications including image classification, image retrieval, object detection, image segmentation and image generation. Compared with traditional machine learning methods which consist of hand-crafted feature extraction and shallow model learning, deep neural networks can learn hierarchical feature representations from low-level to high-level features to get more robust recognition precision. For some specific tasks, researchers prefer to embed feature learning and classification/regression methods into end-to-end models, which can benefit both the accuracy and efficiency. In this thesis, deep models are mainly investigated to study the research problems. Vehicle detection is the most fundamental task in intelligent video surveillance but faces many challenges such as severe illumination and viewpoint variations, occlusions and multi-scale problems. Moreover, learning vehicles’ diverse attributes is also an interesting and valuable problem. To address these tasks and their difficulties, a fast framework of Detection and Annotation for Vehicles (DAVE) is presented, which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): afastvehicleproposalnetwork(FVPN)forvehicle-likeobjectsextraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle’s pose, color and type simultaneously. These two nets are jointly optimized so that the abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the model is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data. The second research problem of the thesis focuses on vehicle re-identification (re-ID). Vehicle re-ID aims to identify a target vehicle in different cameras with non-overlapping views. It has received far less attention in the computer vision community than the prevalent person re-ID problem. Possible reasons for this slow progress are the lack of appropriate research data and the special 3D structure of a vehicle. Previous works have generally focused on some specific views (e.g. front), but these methods are less effective in realistic scenarios where vehicles usually appear in arbitrary view points to cameras. In this thesis, I focus on the uncertainty of vehicle viewpoint in re-ID, proposing four different approaches to address the multi-view vehicle re-ID problem: (1) The Spatially Concatenated ConvNet (SCCN) in an encoder-decoder architecture is proposed to learn transformations across different viewpoints of a vehicle, and then spatially concatenate all the feature maps for further fusing them into a multi-view feature representation. (2) A Cross-View Generative Adversarial Network (XVGAN)is designed to take an input image’s feature as conditional embedding to effectively infer cross-view images. The features of the inferred and original images are combined to learn distance metrics for re-ID.(3)The great advantages of a bi-directional Long Short-Term Memory (LSTM) loop are investigated of modeling transformations across continuous view variation of a vehicle. (4) A Viewpoint-aware Attentive Multi-view Inference (VAMI) model is proposed, adopting a viewpoint-aware attention model to select core regions at different viewpoints and then performing multi-view feature inference by an adversarial training architecture