7,833 research outputs found
Computational Geometry Contributions Applied to Additive Manufacturing
This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows:
(1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains.
(2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser.
(3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in â„ť" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces.
The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Binaural virtual auditory display for music discovery and recommendation
Emerging patterns in audio consumption present renewed opportunity for searching or navigating music via spatial audio interfaces. This thesis examines the potential benefits and considerations for using binaural audio as the sole or principal output interface in a music browsing system. Three areas of enquiry are addressed. Specific advantages and constraints in spatial display of music tracks are explored in preliminary work. A voice-led binaural music discovery prototype is shown to offer a contrasting interactive experience compared to a mono smartspeaker. Results suggest that touch or gestural interaction may be more conducive input modes in the former case. The limit of three binaurally spatialised streams is identified from separate data as a usability threshold for simultaneous presentation of tracks, with no evident advantages derived from visual prompts to aid source discrimination or localisation. The challenge of implementing personalised binaural rendering for end-users of a mobile system is addressed in detail. A custom framework for assessing head-related transfer function (HRTF) selection is applied to data from an approach using 2D rendering on a personal computer. That HRTF selection method is developed to encompass 3D rendering on a mobile device. Evaluation against the same criteria shows encouraging results in reliability, validity, usability and efficiency. Computational analysis of a novel approach for low-cost, real-time, head-tracked binaural rendering demonstrates measurable advantages compared to first order virtual Ambisonics. Further perceptual evaluation establishes working parameters for interactive auditory display use cases. In summation, the renderer and identified tolerances are deployed with a method for synthesised, parametric 3D reverberation (developed through related research) in a final prototype for mobile immersive playlist editing. Task-oriented comparison with a graphical interface reveals high levels of usability and engagement, plus some evidence of enhanced flow state when using the eyes-free binaural system
\{kappa}HGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
The prevalence of tree-like structures, encompassing hierarchical structures
and power law distributions, exists extensively in real-world applications,
including recommendation systems, ecosystems, financial networks, social
networks, etc. Recently, the exploitation of hyperbolic space for tree-likeness
modeling has garnered considerable attention owing to its exponential growth
volume. Compared to the flat Euclidean space, the curved hyperbolic space
provides a more amenable and embeddable room, especially for datasets
exhibiting implicit tree-like architectures. However, the intricate nature of
real-world tree-like data presents a considerable challenge, as it frequently
displays a heterogeneous composition of tree-like, flat, and circular regions.
The direct embedding of such heterogeneous structures into a homogeneous
embedding space (i.e., hyperbolic space) inevitably leads to heavy distortions.
To mitigate the aforementioned shortage, this study endeavors to explore the
curvature between discrete structure and continuous learning space, aiming at
encoding the message conveyed by the network topology in the learning process,
thereby improving tree-likeness modeling. To the end, a curvature-aware
hyperbolic graph convolutional neural network, \{kappa}HGCN, is proposed, which
utilizes the curvature to guide message passing and improve long-range
propagation. Extensive experiments on node classification and link prediction
tasks verify the superiority of the proposal as it consistently outperforms
various competitive models by a large margin.Comment: KDD 202
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Toward Annotation Efficiency in Biased Learning Settings for Natural Language Processing
The goal of this thesis is to improve the feasibility of building applied NLP systems for more diverse and niche real-world use-cases of extracting structured information from text. A core factor in determining this feasibility is the cost of manually annotating enough unbiased labeled data to achieve a desired level of system accuracy, and our goal is to reduce this cost. We focus on reducing this cost by making contributions in two directions: (1) easing the annotation burden by leveraging high-level expert knowledge in addition to labeled examples, thus making approaches more annotation-efficient; and (2) mitigating known biases in cheaper, imperfectly labeled real-world datasets so that we may use them to our advantage. A central theme of this thesis is that high-level expert knowledge about the data and task can allow for biased labeling processes that focus experts on only manually labeling aspects of the data that cannot be easily labeled through cheaper means. This combination allows for more accurate models with less human effort. We conduct our research on this general topic through three diverse problems with immediate applications to real-world settings.
First, we study an applied problem in biased text classification. We encounter a rare-event text classification system that has been deployed for several years. We are tasked with improving this system's performance using only the severely biased incidental feedback provided by the experts over years of system use. We develop a method that combines importance weighting and an unlabeled data imputation scheme that exploits the selection-bias of the feedback to train an unbiased classifier without requiring additional labeled data. We experimentally demonstrate that this method considerably improves the system performance.
Second, we tackle an applied problem in named entity recognition (NER) concerning learning tagging models from data that have very low recall for annotated entities. To solve this issue we propose a novel loss, the Expected Entity Ratio (EER), that uses an uncertain estimate of the proportion of entities in the data to counteract the false-negative bias in the data, encouraging the model to have the correct ratio of entities in expectation. We justify the principles of our approach by providing theory that shows it recovers the true tagging distribution under mild conditions. Additionally we provide extensive empirical results that show it to be practically useful. Empirically, we find that it meets or exceeds performance of state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. We also show that, when combined with our approach, a novel sparse annotation scheme can outperform exhaustive annotation for modest annotation budgets.
Third, we study the challenging problem of syntactic parsing in low-resource languages. We approach the problem from a cross-lingual perspective, building on a state-of-the-art transfer-learning approach that underperforms on ``distant'' languages that have little to no representation in the training corpus. Motivated by the field of syntactic typology, we introduce a general method called Expected Statistic Regularization (ESR) to regularize the parser on distant languages according to their expected typological syntax statistics. We also contribute general approaches for estimating the loss supervision parameters from the task formalism or small amounts of labeled data. We present seven broad classes of descriptive statistic families and provide extensive experimental evidence showing that using these statistics for regularization is complementary to deep learning approaches in low-resource transfer settings.
In conclusion, this thesis contributes approaches for reducing the annotation cost of building applied NLP systems through the use of high-level expert knowledge to impart additional learning signal on models and cope with cheaper biased data. We publish implementations of our methods and results, so that they may facilitate future research and applications. It is our hope that the frameworks proposed in this thesis will help to democratize access to NLP for producing structured information from text in wider-reaching applications by making them faster and cheaper to build
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
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