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Direct learning ofLPVcontrollers from data
No full textIn many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV) plants by linear parametervarying (LPV) models and design controllers based on such representations to regulate the behaviour of the system. The LPV system class offers the representation of NL and TV phenomena as a linear dynamic relationship between input and output signals, which relationship is dependent on some measurable signals, e.g., operating conditions, often called as scheduling variables. For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research. Therefore, it is attractive to synthesize the controller directly from data without the need of modeling the plant and addressing the underlying difficulties. Hence, in this paper, a novel data-driven synthesis scheme is proposed in a stochastic framework to provide a practically applicable solution for synthesizing LPV controllers directly from data. Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided. The effectiveness of the proposed methods is also illustrated by means of an academic example and a real application based simulation case study
From NASGRO to fractals: Representing crack growth in metals
No full textThis paper presents the results of an extensive experimental analysis of the fractal properties of fatigue crack rough surfaces. The analysis of the power-spectral density functions of profilometric traces shows a predominance of the box fractal dimension D = 1.2. This result leads to a particularization of the fatigue crack growth equation based on fractality proposed by the last two authors which is very close to the generalized Frost–Dugdale equation proposed by the first three authors. The two approaches, albeit based
on different initial modelling assumptions, are both very effective in predicting the crack growth rate of short cracks
Technology Diffusion on the International Trade Network
No full textTechnological innovations generate knowledge spillovers—non-innovators benefit through the adoption, imitation, and extension of new technologies. International trade facilitates technology diffusion by providing importing countries access to technical knowledge that they can potentially internalize. Previous studies of the effect of trade on technology diffusion typically only consider the impact of direct (bilateral) trade on indirect measures of technology (e.g., total factor productivity). We contend that the analysis of trade's impact on technology diffusion would be more accurately assessed by using direct measures of specific technologies (e.g., intensity levels) and by allowing for the influence of both the direct and indirect effects of trade in the analysis. The latter is accomplished by modeling the international trade system as a weighted network, which quantifies both direct and indirect trade linkages. Combining trade data with data on the adoption of specific technologies, we find that the network effects of trade play a significant role in technology diffusion. In most cases, countries that are better-connected on the trade network have higher technology intensities. Further support for the importance of trade is provided by the finding that for “outdated” technologies, better-connected countries have lower technology intensities because of their adoption of newer, substitute technologies
Finely-grained annotated datasets for image-based plant phenotyping
No full textImage-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets
GPU-accelerated stochastic predictive control of drinking water networks
No full textDespite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona
A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory
No full textA hierarchical method for the approximate computation of the consensus state of a network of agents is investigated. The method is motivated theoretically by spectral graph theory arguments. In a first phase, the graph is divided into a number of subgraphs with good spectral properties, i.e., a fast convergence toward the local consensus state of each subgraph. To find the subgraphs, suitable clustering methods are used. Then, an auxiliary graph is considered, to determine the final approximation of the consensus state in the original network. A theoretical investigation is performed of cases for which the hierarchical consensus method has a better performance guarantee than the non-hierarchical one (i.e., it requires a smaller number of iterations to guarantee a desired accuracy in the approximation of the consensus state of the original network). Moreover, numerical results demonstrate the effectiveness of the hierarchical consensus method for several case studies modeling real-world networks
Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands.
No full textThe term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies
DiffLQN: Differential Equation Analysis of Layered Queuing Networks
No full textLayered queuing networks are a popular technique in software performance engineering. In this paper we present DiffLQN, a tool for the analysis of networks using ordinary differential equations. It estimates average performance indices such as throughput, utilization, and response time of software and hardware devices. The complexity of computing the solution is independent of the concurrency levels in the model (i.e., thread multiplicities and processing units) and the estimates are theoretically guaranteed to be asymptotically correct for large enough concurrency levels. DiffLQN is designed having in mind compatibility with other tools that support state-of-the-art methods based on mean value analysis
Assessing financial distress dependencies in OTC markets: a new approach using trade repositories data
No full textIn this paper, we study the relationships among financial market sub-segments as a way to identify potential financial distress through increased co-movements among them. To study how sub-markets are mutually co-dependent, we combine granular data on over-the-counter derivatives by trade repositories and the joint probability of distress (JPoD) approach introduced by the International Monetary Fund. We define an indicator that combines several distress drivers and observe that results on co-dependencies are similar to those that would be expected: similarities between financial and contractual terms seem to be responsible for stronger co-movements among sub-markets. However, high values for JPoD even in correspondence of quite dissimilar sub-markets suggest the presence of other drivers that should be investigated in future research. To the best of our knowledge, this is the first empirical study on systemic risk assessment based on micro-founded trade repositories’ data on interest rate swaps