A method for spreading and cutting flexible sheet materials

Accordingly with the invention, during the spreading phase, plies stacked in each cutting position can be sep- arated into different groups by applying one flexible film ply, called "separator film" between groups. The appli- cation of the separator film is provided by means of a "separating spreader". It is desirable to provide an appa- ratus for separating plies that is efficient and does not increase sheet material spreading time. It is an object of the present invention in its preferred embodiment at least to provide a method for applying separating film that min- imize the time required for spread separating film. It is a further object of the present invention in its preferred em- bodiment at least to provide a method for accommodates a separating film at different heights at different cutting positions in the lay-up. It is a another object of the present invention in its preferred embodiment at least to provide a method that ensure that the separating film is applied without both stopping the sheet material spreading and requiring a spreader set-up operation. Moreover in ac- cordance with the present invention, the spreader is pro- vided by an innovative operating method oriented to the minimization of the number of lay-ups formed Indeed, the lower are the spreading time and the cutting time spent to process the current workload, the higher are the productivity performance of the system. Since the cutting patterns are pre-established, it is an object of the present invention to provide a method to compose single lay-up in order to maximize the length of spread plies (i.e. to minimize supply roll changes and the number of distinct spreading operations) as well as to maximize cutting po- sitions heights (i.e. minimize of the number of cutting po- sitions)

The Glue Factories of Casolla: Archaeology of the Structures of a Proto-Industrial Bioclimatic System

In the borough of Casolla, near Caserta, a large number of proto-industrial buildings still exist that were used for the production of animal glue between the end of the 18th century and the beginning of the 20th century. The driers, an integral part of these structures, characterise the urban landscape, emerging with their long, high volumes from the mass of traditional buildings. The position and orientation of the driers of the glue factories (known as collére), as they can still be seen today, were chosen solely for the purpose of achieving a very specific natural regime for the internal microclimate, aimed at maximising air flow and speed in order to accelerate the glue drying process. These formal aspects have been preserved until today, despite the changes made by the owners in order to transform the old driers into modern houses. The glue factories of Casolla, with their driers, constitute a "unicum" from an architectural point of view. A partial and updated re-proposal of the ancient production processes would be unimaginable for conservative purposes, because of their harmfulness to human health and their negative impact on the surrounding environment. That is why their conservation may only take place through refunctionalisation, respecting the typological characteristics and material consistency of the old factories. This type of strategy would be particularly effective due to the specific characteristics of the driers, as it would allow these special characteristics of the cultural landscape of the Caserta area to be preserved

Iterative Assessment and Improvement of DNN Operational Accuracy

Deep Neural Networks (DNN) are nowadays largely adopted in many application domains thanks to their human-like, or even superhuman, performance in specific tasks. However, due to unpredictable/unconsidered operating conditions, unexpected failures show up on field, making the performance of a DNN in operation very different from the one estimated prior to release. In the life cycle of DNN systems, the assessment of accuracy is typically addressed in two ways: offline, via sampling of operational inputs, or online, via pseudo-oracles. The former is considered more expensive due to the need for manual labeling of the sampled inputs. The latter is automatic but less accurate. We believe that emerging iterative industrial-strength life cycle models for Machine Learning systems, like MLOps, offer the possibility to leverage inputs observed in operation not only to provide faithful estimates of a DNN accuracy, but also to improve it through remodeling/retraining actions. We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling techniques to estimate and improve the operational accuracy of a DNN in the iterations of its life cycle. Preliminary results show the benefits of combining the two approaches and integrating them in the DNN life cycle

An exact solution to the TLP problem in a NC Machine

This paper considers a job sequencing problem for a single numerical controlled machining center. It is assumed that all the considered jobs must be processed on a single machine provided with a tool magazine with C positions, that no job requires more than C tools to be completely machined and that the tools may be loaded and unloaded from the tool magazine only when the machining operations for each job are completed. The decisional problem is referred to as the tool loading problem (TLP) and it determines the jobs machining sequence as well as the tools to load in the machine tool magazine before the machining operations on each job may start. In industrial cases where the tool switching time is both significant relative to job processing time and proportional to the number of tool switches, the performance criterion is the minimization of the number of tool switches. This paper demonstrates that the TLP is a symmetric sequencing problem. The authors enrich a branch-and-bound algorithm proposed in literature for the TLP with the new symmetric formulation. Computational experiments show the significant improvement obtained by the novel symmetric formulation of the TLP

Assessing Black-box Test Case Generation Techniques for Microservices

Testing of microservices architectures (MSA) – today a popular software architectural style - demands for automation in its several tasks, like tests generation, prioritization and execution. Automated black-box generation of test cases for MSA currently borrows techniques and tools from the testing of RESTful Web Services. This paper: i) proposes the uTest stateless pairwise combinatorial technique (and its automation tool) for test cases generation for functional and robustness microservices testing, and ii) experimentally compares - with three open-source MSA used as subjects - four state-of-the-art black-box tools conceived for Web Services, adopting evolutionary-, dependencies- and mutation-based generation techniques, and the pro- posed uTest combinatorial tool. The comparison shows little differences in coverage values; uTest pairwise testing achieves better average failure rate with a considerably lower number of tests. Web Services tools do not perform for MSA as well as a tester might expect, highlighting the need for MSA-specific techniques

Assessing Operational Accuracy of CNN-based Image Classifiers using an Oracle Surrogate

Context Assessing the accuracy in operation of a Machine Learning (ML) system for image classification on arbitrary (unlabeled) inputs is hard. This is due to the oracle problem, which impacts the ability of automatically judging the output of the classification, thus hindering the accuracy of the assessment when unlabeled previously unseen inputs are submitted to the system. Objective We propose the Image Classification Oracle Surrogate (ICOS), a technique to automatically evaluate the accuracy in operation of image classifiers based on Convolutional Neural Networks (CNNs). Method To establish whether the classification of an arbitrary image is correct or not, ICOS leverages three knowledge sources: operational input data, training data, and the ML algorithm. Knowledge is expressed through likely invariants - properties which should not be violated by correct classifications. ICOS infers and filters invariants to improve the correct detection of misclassifications, reducing the number of false positives. We evaluate ICOS experimentally on twelve CNNs – using the popular MNIST, CIFAR10, CIFAR100, and ImageNet datasets. We compare it to two alternative strategies, namely cross-referencing and self-checking. Results Experimental results show that ICOS exhibits performance comparable to the other strategies in terms of accuracy, showing higher stability over a variety of CNNs and datasets with different complexity and size. Conclusions ICOS likely invariants are shown to be effective in automatically detecting misclassifications by CNNs used in image classification tasks when the expected output is unknown; ICOS ultimately yields faithful assessments of their accuracy in operation. Knowledge about input data can also be manually incorporated into ICOS, to increase robustness against unexpected phenomena in operation, like label shift

Robust Batch Process Scheduling in Pharmaceutical Industries: A Case Study

none4openT Adamo, G Ghiani, AD Grieco, E GuerrieroAdamo, T; Ghiani, G; Grieco, Ad; Guerriero,

SeaClouds: An Open Reference Architecture for Multi-Cloud Governance

A. Brogi, J. Carrasco, J. Cubo, F. D'Andria, E. Di Nitto, M. Guerriero, D. Pérez, E. Pimentel, J. Soldani. "SeaClouds: An Open Reference Architecture for Multi-Cloud Governance". In B. Tekinerdogan et al. (Eds.): ECSA 2016, LNCS 9839, pp. 334–338, 2016.We present the open reference architecture of the SeaClouds solution. It aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and reconfiguration of app modules over heterogeneous cloud providers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech