322,932 research outputs found
Feature extraction and soft computing methods for aerospace structure defect classification
No full textThis study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method - namely the novel CBIR-based one outperforms all the competitors - and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application
A NAT traversal mechanism for cloud video surveillance applications using WebSocket
No full textThis paper describes a novel Video Surveillance as a Service (VSaaS) architecture. The proposed solution uses an add-on component, named WS-Gateway (WebSocket-based gateway), installed in the client’s private network (along with IP-cameras network). The WebSocket protocol is used to establish a bi-directional communication among the actors in the system. The main advantage of the solution is the overcoming of reachability problems caused by the presence of NATs or Firewalls in the network. A prototype system including one IP-camera, a WS-Gateway running on Android smartphone, a WS-Server built on a Windows system, and a Web-page implementing an user front-end has been tested. The obtained experimental results are compared, in term of latency time, frame loss rate and other implementation features, to other existing solutions, and to the traditional HTTP-Polling used in conjunction with the SSH reverse tunneling to traverse the NAT
Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications
No full textBackground: The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets. Results: We find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to reduce the costs of the communications between different memories (RAM, Cache, Mass, Virtual) and to achieve efficient I/O performance, we design a mass storage structure able to access its data with a high degree of temporal and spatial locality. Then we develop a parallel implementation of the algorithm. We model it as a SPMD system together to a Message- Passing Programming Paradigm. Here, we adopt the high-level message-passing systems MPI (Message Passing Interface) in the version for the Java programming language, MPJ. The parallel processing is organized into four stages: partitioning, communication, agglomeration and mapping. The decomposition of the U-BRAIN algorithm determines the necessity of a communication protocol design among the processors involved. Efficient synchronization design is also discussed. Conclusions: In the context of a collaboration between public and private institutions, the parallel model of U-BRAIN has been implemented and tested on the INTEL XEON E7xxx and E5xxx family of the CRESCO structure of Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), developed in the framework of the European Grid Infrastructure (EGI), a series of efforts to provide access to high-throughput computing resources across Europe using grid computing techniques. The implementation is able to minimize both the memory space and the execution time. The test data used in this study are IPDATA (Irvine Primate splicejunction DATA set), a subset of HS3D (Homo Sapiens Splice Sites Dataset) and a subset of COSMIC (the Catalogue of Somatic Mutations in Cancer). The execution time and the speed-up on IPDATA reach the best values within about 90 processors. Then the parallelization advantage is balanced by the greater cost of non-local communications between the processors. A similar behaviour is evident on HS3D, but at a greater number of processors, so evidencing the direct relationship between data size and parallelization gain. This behaviour is confirmed on COSMIC. Overall, the results obtained show that the pa allel version is up to 30 times faster than the serial one
A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees
No full textMeningitis is an inflammation of the protective membranes covering the brain and the spinal cord. Meningitis can have different causes, and discriminating between meningitis etiologies is still considered a hard task, especially when some specific clinical parameters, mostly derived from blood and cerebrospinal fluid analysis, are not completely available. Although less frequent than its viral version, bacterial meningitis can be fatal, especially when diagnosis is delayed. In addition, often unnecessary antibiotic and/or antiviral treatments are used as a solution, which is not cost or health effective. In this work, we address this issue through the use of machine learning-based methodologies. We consider two distinct cases. In one case, we take into account both blood and cerebrospinal parameters; in the other, we rely exclusively on the blood data. As a result, we have rules and formulas applicable in clinical settings. Both results highlight that a combination of the clinical parameters is required to properly distinguish between the two meningitis etiologies. The results on standard and clinical datasets show high performance. The formulas achieve 100% of sensitivity in detecting a bacterial meningitis
Oboe
No full textDurante el transcurso del s. XIX constructores franceses aplicaron sucesivas innovaciones al diseño del tubo y del mecanismo de llaves del oboe. Dichas innovaciones cristalizaron hacia 1880 en un modelo que, con ligeras variantes, se conserva en los instrumentos actuales. A su vez debe consignarse un fenómeno bastante frecuente en la historia de los instrumentos: un número considerable de ejecutantes se resistieron a adoptar las nuevas propuestas y continuaron utilizando modelos conservadores. El ejemplar de la firma Frat. e Cugino Rampone se enrola en esta categoría. Su datacíón es cercana a 1900. Sin embargo sus características principales, con influencias tanto francesas como austríacas, pertenecen a un período anterior.In the 19 th. c , the oboe key mechanism and tube designs were successively modified by French makers. By 1880, such modifications originated a model which is found with slight variations in the instruments at present. A great number of players refused to adopt the new designs and continued using the old ones. This has been a frequent attitude throughout the history of instruments. The model of the specimen belonging to the firm Frat. e Cugino Rampone is old fashion. Although it dates from ca. 1900, its main features, which how both French and Austrian influence, are those of an earlier period.Perteneciente a la firma Frat. e Cugino Rampone (Milano) su creación data del 1900 aproximadamente.Museo de Instrumentos Musicales Dr. Emilio Azzarin
Shape-based defect classification for non destructive testing
No full textThe aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors
Fast Eddy Current Testing Defect Classification Using Lissajous Figures
No full textIn this paper, we present a fast method for classification of defects detected by eddy current testing (ECT). This is done by using defects derived by lab experiments. For any defect, the ECT magnetic field response for different EC-probe's paths is represented on a complex plane to obtain Lissajous' figures. Their shapes are described through the use of few geometrical parameters forming a feature vector. Such vectors are used as signatures of the defects detected by the probe at different crossing angles and distances from the defect. The effectiveness of the proposed approach is evaluated by measuring the performances of three machine learning-based classifiers (Naïve Bayes, C4.5/J48 Decision Tree, and Multilayer Perceptron neural network), through the following metrics: area under ROC curve, the Matthews correlation coefficient, and F-Measure. The results confirm the usefulness of the proposed approach to defects detection and classification without the need of an overall scanning of the faulty area. So, it is able to minimize the efforts, and, consequently, the cost of an ECT test
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