An Approach to Physical Performance Analysis for Judo

Sport performance analysis is a technique that is becoming every year more important for athletes of every level. Many techniques have been developed to measure and analyse efficiently the performance of athletes in some sports, but in combat sports these techniques found in many times their limits, due to the high interaction between the two opponents during the competition. In this paper the problem will be framed. Moreover the physical performance measurement problem will be analysed and three different techniques to manage it will be presented. All the techniques have been used to analyse the performance of 22 high level Judo athletes

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

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Definition for polarization P and magnetization M fully consistent with Maxwell's equations

Dealing with the project of metamaterials scientists often have to design circuit elements at a subwavelength (or ``microscopic'') scale. At that scale, they use the set of Maxwell's equations in free-space, and neither permittivity ε nor permeability μ are formally defined. However, the objective is to use the unit cells in order to build a bulk material with some desired ``macroscopic'' properties. At that scale the set of Maxwell's equations in matter is adopted. To pass from one approach to the other is not obvious. In this paper we analyse the classic definitions of polarization P and magnetization M, highlighting their limits. Then we propose a definition for P and M fully consistent with Maxwell's equations at any scale

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications

Direct Synthesis of Dual-Parameter Concentric Ring RA with Enhanced Bandwidth

Reflectarray antennas (RAs) are nowadays a quite popular technology, used in several applications, due to a significant number of attractive properties, such as low cost, low weight, conformal deployment, and the possibility of introducing suitable reconfigurable capabilities. Unfortunately, they present also some intrinsic limitations and drawbacks compared with other solutions and, in particular, a relatively narrow bandwidth; that of course could be enlarged, but generally with a drastic increase of the structure complexity. The objective of this work is the design of a single-layer passive reflectarray, in which the reradiated elements have no conventional shape and enough degrees of freedom to compensate both the spatial and frequency phase variation of the reradiated field. In particular, here we focus on a reradiating element consisting in two concentric square rings in which two different and quite independent geometric parameters are varied

Hybridization strategy for microstrip antenna optimization

In the exploding growth of radio mobile and wireless communication applications, microstrip antennas with its advantages of low cost and flexible fabrications, emerge as the most suitable candidate. The direct antenna synthesis could, however do not result in the optimal antenna configuration, and therefore a possible alternative is considering the problem of optimizing the antenna as a system of uncertainty, in which each set of geometrical parameters returns a totally different response; the best set, i.e. the one that gives the best antenna performances, can be obtained using global optimizers, as evolutionary algorithms. The main drawback of this approach is that it is really time and memory consuming. In this article, a technique based on the hybridization between Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)is introduced with the aim of reducing this nimerical cost and implemented to optimize a dual-annular ring proximity coupled feed antenna

Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring

The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires the collection of accurate data across various temporal and spatial scales. Accordingly, monitoring systems are seen as central tools for ecosystem-based environmental management, helping on one hand to accurately describe the water column and substrate biophysical properties, and on the other hand to correctly steer sustainability policies by providing timely and useful information to decision-makers. A robust and intelligent sensor network that can adjust and be adapted to different and changing environmental or management demands would revolutionize our capacity to wove accurately model, predict, and manage human impacts on our coastal, marine, and other similar environments. In this paper advanced evolutionary techniques are applied to optimize the design of an innovative energy harvesting device for marine applications. The authors implement an enhanced technique in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization and Genetic Algorithms. Here, this hybrid procedure is applied to a power buoy designed for marine environmental monitoring applications in order to optimize the recovered energy from sea-wave, by selecting the optimal device configuration

Hybridization strategy for microstrip antenna optimization

In the exploding growth of radio mobile and wireless communication applications, microstrip antennas with its advantages of low cost and flexible fabrications, emerge as the most suitable candidate. The direct antenna synthesis could, however do not result in the optimal antenna configuration, and therefore a possible alternative is considering the problem of optimizing the antenna as a system of uncertainty, in which each set of geometrical parameters returns a totally different response; the best set, i.e. the one that gives the best antenna performances, can be obtained using global optimizers, as evolutionary algorithms. The main drawback of this approach is that it is really time and memory consuming. In this article, a technique based on the hybridization between Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)is introduced with the aim of reducing this nimerical cost and implemented to optimize a dual-annular ring proximity coupled feed antenna

Hard color-singlet exchange in dijet events in proton-proton collisions at root s=13 TeV

Events where the two leading jets are separated by a pseudorapidity interval devoid of particle activity, known as jet-gap-jet events, are studied in proton-proton collisions at root s = 13 TeV. The signature is expected from hard color-singlet exchange. Each of the highest transverse momentum (p(T)) jets must have p(T)(jet) > 40 GeV and pseudorapidity 1.4 0.2 GeV in the interval vertical bar eta vertical bar < 1 between the jets are observed in excess of calculations that assume only color-exchange. The fraction of events produced via color-singlet exchange, f(CSE), is measured as a function of p(T)(jet2), the pseudorapidity difference between the two leading jets, and the azimuthal angular separation between the two leading jets. The fraction f(CSE) has values of 0.4-1.0%. The results are compared with previous measurements and with predictions from perturbative quantum chromodynamics. In addition, the first study of jet-gap-jet events detected in association with an intact proton using a subsample of events with an integrated luminosity of 0.40 pb(-1) is presented. The intact protons are detected with the Roman pot detectors of the TOTEM experiment. The f(CSE) in this sample is 2.91 +/- 0.70(stat)(-1.01)(+1.08)(syst) times larger than that for inclusive dijet production in dijets with similar kinematics.Peer reviewe