Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.

Labeling methods for association rule clustering

Although association mining has been highlighted in the last years, the huge number of rules that are generated hamper its use. To overcome this problem, many post-processing approaches were suggested, such as clustering, which organizes the rules in groups that contain, somehow, similar knowledge. Nevertheless, clustering can aid the user only if good descriptors be associated with each group. This is a relevant issue, since the labels will provide to the user a view of the topics to be explored, helping to guide its search. This is interesting, for example, when the user doesn't have, a priori, an idea where to start. Thus, the analysis of different labeling methods for association rule clustering is important. Considering the exposed arguments, this paper analyzes some labeling methods through two measures that are proposed. One of them, Precision, measures how much the methods can find labels that represent as accurately as possible the rules contained in its group and Repetition Frequency determines how the labels are distributed along the clusters. As a result, it was possible to identify the methods and the domain organizations with the best performances that can be applied in clusters of association rules.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Predição da potência aeróbia (VO 2máx) de crianças e adolescentes em teste incremental na esteira rolante

The maximal oxygen uptake (VO2max) is the maximal quantity of energy that can be produced by the aerobic metabolism in certain time unity. It can be determined direct or indirectly by predictive equations. The objective of this study was to make a specific predictive equation to determine the VO 2max from boys aged 10-16 years-old. Forty-two boys underwent a treadmill running ergospirometric test, with the initial velocity set at 9 km/h, until voluntary exhaustion. By the multiple linear regression was possible to develop the following equation for the indirect determination of the VO 2max: VO2max (ml/min) = -1574.06 + (141.38 x Vpeak) + (48.34 * Body mass), with standard error of estimate = 191.5 ml/min (4.10 ml/kg/min) and coefficient of determination = 0.934. We suggest that this formula is appropriate to predict VO2max for this population.

Relationship between running intensity, muscle activation, and stride kinematics during an incremental protocol

Objective: To analyze the effect of running intensity on stride length (SL), stride frequency (SF), stride time (ST) and the electromyographic signal of the rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), tibialis anterior (TA), biceps femoris (BF) and gastrocnemius lateralis (GL) muscles. Methods: Nine well-trained runners performed an incremental protocol with an initial velocity of 10km.h-1, and increments of 1km.h-1 every 3minutes until exhaustion. The electromyographic activity, SL, SF, ST, inter-stride coefficient of variation, and association between kinematic and electromyographic parameters were calculated at 60%, 80% and 100% of maximum running velocity. Results: SL, SF and electromyographic activity of the RF, VM, VL and GL increased and the ST decreased with increased running speed. Electromyographic variability of VL and VM was higher than GL, and variability was lower in TA than all other muscles. The inter-stride variability of muscle activation was associated with kinematic parameters, and their variability, differently as running speed increased. Conclusion: The incremental protocol increased electromyographic activity differently among lower limb muscles; increased SF and SL, and decreased ST, without changing the variability of these variables. Muscle activation variability was correlated with kinematic parameters, but the relationships among these measures varied with running intensity. © 2013 .

Serum activity of creatine kinase and aminotransferase aspartate enzymes of horses submitted to muscle biopsy and incremental jump test

The objective was to evaluate serum activity of the enzymes creatine kinase (CK) and aspartate aminotransferase (AST), which are leakage enzymes responsive to muscle injury, of athletic horses that underwent muscle biopsy and incremental jump test (IJT) involving incremental jumps. The animals were grouped as follows: the first group, horses with history of superior performance (SP); the second, with a history of inferior performance (IP); and lastly, a control group (CG). All groups underwent biopsy of the gluteus medius muscle, while groups SP and IP were also submitted to the incremental jump test (IJT) 24 hours after biopsy. The IJT consisted of three stages with 40 jumps each, where jump height increased progressively, from 40 to 60 and last, 80cm. Blood samples were drawn before biopsy, and 6 and 24 hours after the exercise as well. The levels of CK serum activity increased 6 hours after exercise and decreased 24 hours later in all groups, including CG. AST activity did not increase after biopsy and exercise. There was no increase of both enzyme activities that could be attributed to the exercise, possibly due to exercise short duration and/or low intensity. We conclude that the muscle biopsy was able to show that there was enough stimulus to cause CK enzyme leakage into the plasma, and consequent detection of increased serum activity, while the incremental jump test did not.

Metrics to support the evaluation of association rule clustering

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.

Análise de parâmetros eletromiográficos e cinemáticos durante teste incremental de corrida: Priscila de Brito Silva. -

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Protocolo incremental de corrida: comportamento de variáveis eletromiográficas, cinemáticas e metabólicas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of cardiopulmonary and metabolic variables measured during laboratory and sport-specific incremental tests for table tennis performance prediction

Purpose. - The purposes of this study were: i) to compare the physiological responses measured during a specific table tennis incremental test with the physiological responses measured during cycling, arm cranking, and treadmill running tests; and ii) to verify the accuracy of table tennis performance prediction based on the physiological responses from these tests.Methods. - Eleven national level male table tennis players participated in the study and undertook incremental tests using ergometers. Table tennis performance was defined as the ranking obtained during a simulated tournament between the participants.Results. - In general, peak values for physiological variables (e.g., (V) over dotO(2PEAK) and [La]PEAK) were significantly lower (P < 0.05) in the specific test (e.g., (V) over dotO(2PEAK) = 39.9 +/- 1.5 ml.kg(-1) per minute and [La]PEAK = 6.4 +/- 0.5 mmol.L-1) than during cycling (e.g., (V) over dotO(2PEAK) = 41.3 +/- 1.4 ml.kg(-1) per minute and [La]PEAK = 10.2 +/- 0.7 mmol.L-1) or running (e.g., (V) over dotO(2PEAK) = 43.9 +/- 1.5 ml.kg(-1) per minute and [La]PEAK = 10.0 +/- 0.7 mmol.L-1), but higher than during arm cranking (e.g., (V) over dotO(2PEAK) = 26.6 +/- 1.6 ml.kg(-1) per minute and [La]PEAK = 8.9 +/- 0.6 mmol.L-1). At respiratory compensation point intensity (RCP), only the variables measured on arm cranking were lower (P < 0.05) than on the other ergometers. Stepwise multiple regression analysis showed significant correlation between table tennis performance and lactate concentration ([La]) and also rate of perceived effort (RPE) at RCP during cycling (r = 0.89; P < 0.05).Conclusion. - In conclusion, the significant differences obtained between the specific and laboratory ergometers demonstrate the need to use a specific test to measure physiological parameters in table tennis and the physiological parameters measured, independent of the ergometer used, are unable to predict table tennis performance. (C) 2013 Elsevier Masson SAS. All rights reserved.