A hybrid supervised/unsupervised machine learning approach to solar flare prediction

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data

Coarse correlated equilibria for continuous time mean field games in open loop strategies

In the framework of continuous time symmetric stochastic differential games in open loop strategies, we introduce a generalization of mean field game solution, called coarse correlated solution. This can be seen as the analogue of a coarse correlated equilibrium in the $N$-player game, where a moderator randomly generates a strategy profile and asks the players to pre-commit to such strategies before disclosing them privately to each one of them; such a profile is a coarse correlated equilibrium if no player has an incentive to unilaterally deviate. We justify our definition by showing that a coarse correlated solution for the mean field game induces a sequence of approximate coarse correlated equilibria with vanishing error for the underlying $N$-player games. Existence of coarse correlated solutions for the mean field game is proved by means of a minimax theorem. An example with explicit solutions is discussed as well

using design geometrical features to develop an analytical cost estimation method for axisymmetric components in open die forging

Abstract Hot forging is an industrial process where a metal piece is formed through a series of dies which permanently change the shape of the part. Open-die forging is a particular type of hot forging in which the used dies are generally flat and the part to be formed has a simple shape. Manufacturing cost estimation is a well-debated topic, especially for traditional manufacturing technologies. However, only few models are available in scientific literature for the open-die forging process. This lack is due to the complexity of the process, characterized by a low level of automation and a high degree of expertise required to develop the process. The paper proposes an analytical model for the cost estimation of axisymmetric components realized using open die-forging. The model uses as input the geometrical features of the part (e.g. dimensions, shape, material and tolerances), and gives as output: (i) the time required for the process development, (ii) the amount of material needed for the part processing and, (iii) the forging machine size/type, from the cutting of the billet to the piece deformation. Two cylindrical discs have been analysed for validating the proposed cost estimation model. The case studies show that the cost models give an accurate result in terms of cost breakdown, allowing the designer a quick calculation of process costs

Using engineering documentation to create a data framework for life cycle inventory of welded structures

Abstract Welding is considered an energy-intensive manufacturing system and it represents one of the most impacting construction process. The paper aims to define a structured data framework for life cycle inventory of a welding process starting from engineering and design documentation. The use of design documentation allows to perform robust LCA analysis which permits to compare the environmental performances of the most widely used welding technologies early in the design process. The necessary information to fill the data framework can be retrieved by available documentation developed in the preliminary design phase allowing to anticipate the life cycle analysis before the construction phase. A ship hull structure designed to be manufactured by the use of GMAW and GTAW welding processes has been analyzed as case study. The use of data framework facilitates the inventory phase creating a consistent and robust inventory for LCA

A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics

In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting that formalizes weighted classification metrics and then allows the construction of losses that drive the model to optimize these metrics of interest. After a detailed theoretical analysis, we show that our framework includes as particular instances well-established approaches such as classical cost-sensitive learning, weighted cross entropy loss functions and value-weighted skill scores

Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-of-sight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still - and will likely remain - a predominantly probabilistic challenge

Reliability-oriented resource management for High-Performance Computing

Reliability is an increasingly pressing issue for High-Performance Computing systems, as failures are a threat to large-scale applications, for which an even single run may incur significant energy and billing costs. Currently, application developers need to address reliability explicitly, by integrating application-specific checkpoint/restore mechanisms. However, the application alone cannot exploit system knowledge, which is not the case for system-wide resource management systems. In this paper, we propose a reliability-oriented policy that can increase significantly component reliability by combining checkpoint/restore mechanisms exploitation and proactive resource management policies

Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord

Defaults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal systems is a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with neither contrast agent nor a destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is a crucial tool for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries. In particular, it should be an optimal tool to resolve the entangled relationship between VN and neuronal system.Comment: 15 pages, 6 figure

cost estimation method for gas turbine in conceptual design phase

Abstract Introduction of new gas turbine machines on market is a complex project that requires optimization of different performance parameters such as power, efficiency, maintenance plan, product cost and life. The ability to control cost and impact on performances and life strongly decreases from conceptual to detailed design phase. Actually, 80 % of product's cost and performances are committed based on decisions made in conceptual design. This Paper describes a systematic procedure to estimate the cost of multiple design alternatives during conceptual design phase, comparing different cross sections for gas turbine solutions. Examples of parametric costing tool for part family will be described, to show the approach that allows to estimate costs in conceptual design phase, when detailed design has not been developed and lack of information is a daily topic. The idea is to be able to read design information of each part from an enhanced cross section and enter parametric costing tool to have a preliminary cost estimation in conceptual phase. Doing that for each part or module present, it will be possible to estimate total cost of the product. The scope is to create an internal database where the whole know-how and best practices are stored. This database can be examined in early program stages, to reduce time to market and avoid pursuing solutions that would not be viable or convenient, in a sort of digital twin approach. Another positive aspect pursued and presented, is the positive impact on engineering productivity, that directly reflects on program development cost