Multivariable teaching-learning-based optimization (MTLBO) algorithm for estimating the structural parameters of the buried mass by magnetic data

U ovom radu je predstavljen prirodno utemeljen multivarijabilni algoritam optimizacije poučavanjem-učenjem (MTLBO). MTLBO algoritam tijekom iterativnog postupka može procijeniti najbolje vrijednosti parametara podzemnih struktura (model) u višepredmetnom problemu. Algoritam djeluje u dvije računske faze: fazi učitelja i fazi učenika. Glavna svrha algoritma MTLBO je mijenjati naučene vrijednosti te poboljšavajući tako vrijednosti parametara modela dovesti do optimalnog rješenja. Varijable svakog učenika (model) su: dubina (z), koeficijent amplitude (k), faktor oblika (q), kut učinkovite magnetizacije (θ) i parametri osi (x0). U radu je korištena MTLBO metoda na podacima magnetskih anomalija uzrokovanih podzemnim strukturama jednostavnog geometrijskog oblika, poput sfere i vodoravno postavljenog cilindra. Učinkovitost MTLBO metode također je proučavana na šumom kontaminiranim sintetičkim podacima, budući da su dobiveni prihvatljivi rezultati. MTLBO metoda je primijenjena za interpretaciju četiri profila magnetske anomalije u Iranu, Brazilu i Indiji.This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (θ) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India

Geoacoustic seafloor exploration with a towed array in a shallow water area of the Strait of Sicily (2)

Acoustic propagation in shallow water is greatly dependent on the geoacoustic properties of the seabottom. This paper exploits this dependence for estimating geoacoustic sediment properties from the bottom acoustic returns of known signals received on a hydrophone line array. There are two major issues in this approach: one is the feasibility of acoustic inversion with a limited aperture line array, the other is related to the knowledge of the geometry of the experimental configuration. To test the feasibility of this approach, a 40-hydrophone4-m spaced towed array together with a low-frequency acoustic source, was operated at a shallow water site in the Strait of Sicily. In order to estimate the array deformation in real time, it has been equipped with a set of nonacoustic positioning sensors (compasses, tiltmeters, pressure gauges). The acoustic data were inverted using two complementary approaches: a genetic algorithm (GA) like approach and a radial basis functions (RBF) inversion scheme. More traditional methods, based on core sampling, seismic survey and geophone data, together with Hamilton’s regression curves, have also been employed on the same tracks, in order to provide a ground truth reference environment. The results of the experiment, can be summarized as follows: 1) the towed array movement is not negligible for the application considered and the use of positioning sensors are essential for a proper acoustic inversion, 2) the inversion with GA and RBF are in good qualitative agreement with the ground truth model, and 3) the GA scheme tends to have better stability properties. On the other hand, repeated inversion of successive field measurements requires much less computational effort with RBF.The authors wish to acknowledge the master and crew of the RN ALLIANCE and the SACLANT Centre Engineering Department for their outstanding respective contributions in the leadership, sea-going operation and equipment preparation before and during the sea trial. The support of E. Dias and E. Coelho from the Hydrographic Institute, Lisbon, on the acquisition of the nonacoustic data and of P. Gershoft, SACLANT Centre, on genetic algorithms setup, are also appreciated. The authors wish also to express their appreciation to the anonymous reviewers, whose comments have greatly helped to reshape the second draft of this paper, and hopefully to improve its readability

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination

Applications on Ultrasonic Wave

This book presents applications on the ultrasonic wave for material characterization and nondestructive evaluations. It could be of interest to the researchers and students who are studying on the fields of ultrasonic waves