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Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent transportation systems represent innovative solutions for traffic congestion minimization, mobility improvements and safety enhancement. These systems require various inputs about vehicles and traffic state. Vehicle re-identification systems based on video cameras are most popular; however, more strict privacy policy necessitates depersonalized vehicle re-identification systems. Promising research for depersonalized vehicle re-identification systems involves leveraging the captured unique distortions induced in the Earth's magnetic field by passing vehicles. Employing anisotropic magneto-resistive sensors embedded in the road surface system captures vehicle magnetic signatures for similarity evaluation. A novel vehicle re-identification algorithm utilizing Euclidean distances and Pearson correlation coefficients is analyzed, and performance is evaluated. Initial processing is applied on registered magnetic signatures, useful features for decision making are extracted, different classification algorithms are applied and prediction accuracy is checked. The results demonstrate the effectiveness of our approach, achieving 97% accuracy in vehicle re-identification for a subset of 300 different vehicles passing the sensor a few times. [ABSTRACT FROM AUTHOR]

Magnetic methods of exploration have proved to be efficient and have potential in the gold mineralization industry. New magnetic processing technologies aid in improving the process of interpretation and gold opportunity identification. In this work, we show the possible application of combined digital magnetic filters to explore new gold mineralization localities with application to a well-known Au mineralization zone. Um Garayat (UG) region, southeastern desert, Egypt, is an ancient example of a potential area for gold mining. Modern analysis showed that other types of mineral concentrations are present. So, exploiting the magnetic signature of the area for future investment is of great interest. The old UG gold mine is characterized by volcanic and tectonic features such as faulting and folding that affect the arc sedimentary rock sequence of repeated deformation stages. A thorough geophysical effort has been carried out around the old gold mine in the UG area to explore the extension of mineralized ore deposits. A detailed geophysical survey using magnetics was carried out in this study together with the available aeromagnetic data. Field data sets on appropriate sites were measured, processed, and evaluated by suitable software. High magnetic anomalies were detected based on grid filter analysis and contact occurrence maps as marked as possible ore deposits after satisfying the geologic conditions for gold formation. An integrated understanding of attained results revealed that the new possible ore deposits are related directly to fault and fracture zones in the shape of lenses of variable thickness in this zone. Results show that newly detected mineral occurrences in the UG area are also controlled by major faults and hydrothermal solution enrichment along fault zones at a depth ranging from 20 to 70 m. Favorable fault/joint mineralized places were located. The relationship between the Au-quartz vein's strike direction compared with magnetic anomaly lineament analysis was studied. Search for new sources of Au and other mineral deposits in addition to quartz veins is needed as pockets of accumulated mineral-rich rock fragments are deposited in drainage wadis and fault/joint zones because of hydrothermal solution enrichment. [ABSTRACT FROM AUTHOR]

Demagnetization of ferromagnetic vessels, or deperming, is a common practice used to reduce the magnetic signature. The general approach for ship deperming presented in the literature is the anhysteretic protocol, where the applied field is reduced at equidistant intervals. In reality, the method involves the complications of having a bias (Earth’s magnetic) field present and the practical impossibility of magnetizing the ship to saturation. When anhysteretic demagnetization cannot be applied, past experience is used to empirically estimate the necessary magnetic treatment for each vessel. In this article, we examine the ship deperming on a fundamental basis by describing it using the Jiles–Atherton (J-A) model. The ship model parameters were estimated by inversion from measurements taken at certain points of the demagnetization cycle. The model is useful for testing different protocols of, in general, not-unique deperming operation. The proposed J-A analysis of hysteresis offers a theoretical basis to predict the demagnetization results a priori. The model simulation study of the demagnetization process is demonstrated with the measurements from HMCS Toronto. [ABSTRACT FROM AUTHOR]

Underwater magnetic signature of sea vessel is an important characteristic in mine threat estimation. Traditionally, the vessel’s signal is measured with the help of stationary magnetic range equipped with bottom-mounted magnetic sensors. However, such a range may be unavailable near vessel’s operating area. In this work, we investigate a capability of underwater magnetic signature estimation basing on the results of overhead magnetic mapping of the vessel by a portable total field magnetic sensor installed on a light aerial platform. A magnetic model of the vessel is represented by an array of point magnetic dipoles distributed over steel hull and clusters of ferromagnetic equipment. The model parameters are estimated by fitting the calculated magnetic field produced by the model to a measured overhead magnetic anomaly map. Once these parameters are determined, the vector magnetic field produced by the object may be calculated at any point both in “near” and “far”-field zones. To confirm this approach, we built a steel model ship in a scale of around 1:100. Dedicated setup enables us to measure the magnetic field produced by the model from below and from above. Our experimental results show less than 20% difference between the magnetic signature predicted by the model and measured directly below it. Such accuracy is quite acceptable for this specific application and, therefore, the proposed method can be treated as a viable alternative to traditional magnetic ranging. [ABSTRACT FROM AUTHOR]

In this letter, a deep-learning-based neural network for magnetic dipole modeling (MDMnet) is introduced in the framework of dc magnetic cleanliness for space missions. The developed method targets modeling the static magnetic signature of a spacecraft unit that is obtained during the unit-level characterization stage of the extensive prelaunch electromagnetic compatibility test campaign. By employing synthetic magnetic flux density data generated by virtual dipole sources, the MDMnet can be trained to accurately estimate the magnetic parameters of real equipment based on its near magnetic flux density measurements. The target of the deep learning algorithm is, on the one hand, to effectively minimize the prediction errors (loss function) throughout the training process and, on the other hand, to enable the generalization of the model predictions, i.e., exhibit accurate model estimations with unseen magnetic induction data. Extensive simulations toward the stabilization of the MDMnet hyperparameters are outlined, and indicative model inferences employing artificial magnetic flux density data are carried out. Finally, the MDMnet can achieve a predictive accuracy of 0.8 mm with respect to the dipole localization and 1% with respect to the magnetic induction magnitude, verifying the potency of the developed method. [ABSTRACT FROM AUTHOR]

Accurate magnetic-signature prediction is the most challenging task in the path of active magnetic-signature minimization. This article discusses the effect of the change in hull thickness, relative permeability, and radius of the object, considering the detailed understanding of the magnetic signatures. The traditional magnetic experiment-based method using a 12-coil structure is implemented for a double hull vessel. However, the time required and effort involved in predicting the magnetic signature is much higher. Hence, a multiple linear regression-based statistical machine-learning (ML) technique for the magnetic signature prediction is proposed. The nonlinear magnetic behavior of the naval vessel is assumed to be linear, because the hysteresis curve in the range of the earth’s ambient field is linear. Hence, a linear relationship is developed between the magnetic signatures and each component of the ambient earth’s magnetic field. The ML-based model also considers the crosstalk among the longitudinal, athwartship, and vertical signatures. The predicted magnetic signatures using the ML technique and the experiment-based techniques are compared. The ML method is found to be accurate, fast, and precise compared with the laboratory-based method, which has strict and continuous requirements for hardware magnetic facility. This method is very simple to implement and directly contributes to the development of the real-time degaussing current calculation algorithm. [ABSTRACT FROM AUTHOR]

The spacecraft-level magnetic budget in the framework of dc magnetic cleanliness programs of space missions is studied and analyzed. This analysis follows the unit-level magnetic characterization test campaigns that are performed in space missions with challenging magnetic cleanliness requirements. Taking into consideration the output of these test results, the total magnetic signature of the spacecraft may be estimated at the location of sensitive instruments or potential magnetically susceptible units that are included in the space mission. In this letter, a method that significantly reduces the total magnetic field generated by the spacecraft is investigated. The purpose of the algorithm is to minimize the system-level magnetic signature at specific points of interest and at areas around the spacecraft in order to conform to the cleanliness requirements. This analysis is based on repositioning the equipment inside the spacecraft structure in order to reduce the total generated magnetic field. Indicative simulations of the synthetic magnetic sources are carried out, validating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a fast and efficient method for magnetic signature reduction of underwater vessels. The magnetic signature reduction is required to protect the ferromagnetic vessels from magnetic anomaly detectors and mines. We propose a novel machine learning-based approach for degaussing of the vessel. This method adds a degree of bias to the evaluated coefficients in order to handle inherent multicollinearity issues. The proposed algorithm is efficient in terms of computational efforts, speed, and accuracy. More than 90% of signature reduction is achieved, assuming that the signature predicted is accurate. The proposed method is validated for a simulated model of prototype submarine. [ABSTRACT FROM AUTHOR]

Ship degaussing (DG) is the process of reducing or canceling magnetic fields from a ship by applying an opposing field to it. The most practical and controllable ship DG process is to generate a magnetic field by passing a current through sets of loop coils around the ship. The challenge is to design and install a DG system on board the ship in order to compensate for the ship's magnetic signature to a specified target within a safe limit. In this paper, a brief concept of a ship's DG system and magnetic disturbance of ferromagnetic materials are briefly discussed. We will then qualitatively measure the magnetic field disturbance caused by a ship in Malaysian waters by carrying out ranging works, DG process and finally evaluate the performance of newly installed DG system. This study shows that the ranging process has been carried out successfully and the performance of the new DG system is evaluated based on the capability of the DG system to reduce the magnitude of ship's magnetic fields in the tropical region of Malaysian waters. [ABSTRACT FROM AUTHOR]

To study the magnetic signature of submarines and improve the performance of magnetic anomaly detectors in the antisubmarine warfare, the generation mechanism of the submarine magnetic field (SMF) is first introduced. Magnetic fields produced by submarines are classified into five major types by the cause and state of submarines. Besides, the prediction model of the SMF at low altitude is proposed. Based on the surface integral method, the continuation formula of the magnetic field is derived from the vector potential. According to the magnetic field of a real mockup, the distribution of the SMF is obtained by the continuation formula, and the data of the magnetic anomaly caused by submarines are produced at different heights. Then, the spatial signature of the magnetic anomaly is analyzed. The result of analysis shows that the magnetic anomaly of ferromagnetic submarines has evident characteristics. The distribution of the magnetic anomaly is spatially symmetrical, and the peak values of its components and total field occur in special positions. Furthermore, the signal strength of the magnetic anomaly is sufficiently large to be detected. So the identification of underwater magnetic target can be realized by the special magnetic signature. [ABSTRACT FROM PUBLISHER]

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an equipment dedicated to the monitoring of synchronous generator through the measurements of the external magnetic field time derivative. The equipment uses a methodology which can be an additional technique to those applied currently on the faults detection, mainly when monitoring the incipient faults type. Based on frequency spectra (magnetic signatures), the faults detection is performed by the analysis of the components evolution. As it is not possible to test the ability of the equipment in detecting several types of faults of generators faults in a power plant, a testbench was constructed for this purpose. Faults inserted deliberately in the generators of the testbench are detected by the equipment, showing its capacity. This equipment has been also installed in power plants. [ABSTRACT FROM AUTHOR]

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1×2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm. [ABSTRACT FROM AUTHOR]

This paper concerns a coupled analysis of the stray magnetic field and the external housing vibration of a synchronous machine to detect rotor short-circuit faults. Indeed, in addition to the possibility to measure these parameters with non-invasive equipment, these two measures depend on the air-gap flux density that is directly impacted by the short circuits of any nature. Hence, correlating these two quantities, it is achievable to confirm or discard an eventual motor fault. This method allows both to develop an analysis of non-invasive manner and to establish the magnetic state of a synchronous generator in normal operation. The experimental survey of stray magnetic field and housing vibration spectrum is shown as a promising alternative at low cost and easy to implement and to determine, for instance, rotor turn-to-turn winding faults. Faulty and healthy signatures are drawn from the measured spectra to determine a degree of healthy state of the synchronous machine. [ABSTRACT FROM PUBLISHER]

A dipole equivalent modeling approach and experimental results, for relatively small magnetic sources such as servomotors, are presented. Two modeling schemes, viz. the dual permanent magnetic dipole (DuPMaD) and the permanent magnetic dipole (PMaD), are proposed. PMaD assumes only one equivalent magnetic dipole within the physical dimension of the source component, whereas DuPMaD employs a second magnetic dipole with a view to characterize the field more accurately. The equivalent dipole moment vectors and their positions are estimated via fitting experimental data collected on three orthogonal planes around the specimen. Results show good agreements between the model outputs and the experimental data, with both the DuPMaD and the PMaD schemes performing closely in terms of modeling accuracy. Such models may be used to estimate and minimize magnetic interference in various applications such as in geomagnetic surveying using unmanned aerial vehicles considered in this paper. [ABSTRACT FROM PUBLISHER]

The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data. [ABSTRACT FROM AUTHOR]

In order to analyze the magnetic signature of a naval ship, there was employed a physical scale model of the respective vessel. The model was built to a scale of 1:100, respecting the geometric similarity criteria, in terms of main dimensions proportionality. A physical similarity criterion characteristic to the magnetic signature issue was formulated, taking into account the ratio between the hull sheet thickness and the electric and magnetic properties of the ship and the model, respectively. There have been performed a large volume of triaxial measurements of the magnetic signature, to acquire the magnetic field distribution for an horizontal plane situated below the hull, at reference depth. Based on the analysis of magnetic signature components of the model ship, some conclusions regarding their shape can be drawn. [ABSTRACT FROM AUTHOR]

In general, currently employed vehicle classification algorithms based on the magnetic signature can distinguish among only a few vehicle classes. The work presents a new approach to this problem. A set of characteristic parameters measurable from the magnetic signature and limits of their uncertainty intervals are determined independently for each predefined class. The source of information on the vehicle parameters is its magnetic signature measured in a system that enables independent measurement of two signals, i.e. changes in the active and reactive component of the inductive loop impedance caused by a passing vehicle. These innovations result in high selective classification system, which utilizes over a dozen vehicle classes. The evaluation of the proposed approach was carried out for good vehicles consisting of 2-axle tractor and a 3-axle semi-trailer. [ABSTRACT FROM AUTHOR]

Location-dependent differences of ambient magnetic fields inside a building can be used to estimate location. In this paper, an inertial/magnetic sensor is attached to a belt position and its location is estimated using the ambient magnetic field. The walking distance is estimated using the linear relationship between the walking step length and the maximum acceleration during the step. The magnetic field data during walking is compared with a pre-collected magnetic signature. In this process, calibration steps are required for two person-dependent parameters : the walking step length estimation parameter and the hard iron parameter. An adaptive algorithm is proposed, in which these person-dependent parameters are estimated in addition to the location. Thus no person-dependent parameter calibration process is required. Through experiments, it is shown that the location and parameters are estimated accurately. [ABSTRACT FROM AUTHOR]

A novel magnetic signature attenuation technique based on reconfiguring the location and orientation of the onboard magnetic sources of an unmanned aircraft system (UAS) is presented in this paper. The UAS, GeoSurv II, is intended for high-resolution aeromagnetic survey which requires the magnetic signature of the aircraft to be very low. Genetic algorithm (GA) is used to find an optimum configuration given multiple objective functions motivated by the application. The magnetic field contribution from a single servomotor onboard GeoSurv II is modeled as a single permanent magnet dipole, which is then used to build the cost function for the GA routine. The optimization/simulation outcome suggests very little alteration in the current configuration of the GeoSurv II servomotors resulting in a substantial improvement of the overall magnetic signature of the UAS. The simulation results are validated by practical experimentation. The experimental results, in addition to the simulation results, further confirm that the GA optimized configuration substantially outperforms the current configuration in terms of magnetic signature of GeoSurv II. [ABSTRACT FROM PUBLISHER]

This paper presents a method for the identification of winding failures in induction motors. The types of failures include unbalanced currents flowing into the motor and short-circuit of the winding. The radiated magnetic field of a typical induction motor was studied while various types of failures applied to the machine. The implementation was performed by applying different types of unbalanced currents flow into the machine. The fields were obtained from both numerical finite-element simulations as well as from experimental setups. The turn to terminal and turn to turn short-circuit of the motor's winding were studied. The frequency response of the 3-D finite-element (3DFE) model of the motor was implemented up to high-order frequencies. The numerical results were compared with the measurement results. The fields with unbalanced currents and short-circuit conditions were identified by studying the harmonic orders of the radiated magnetic fields. This was also implemented using artificial neural networks (ANN). The results show that the signature study of the experimental as well as the simulation models can be utilized for failure identification in electric motors with a high level of accuracy. [ABSTRACT FROM PUBLISHER]

Geomagnetic prospection is an efficient and environmentally friendly geophysical method for the analysis of the magnetic minerals' distribution in the subsurface. High-resolution measurements require on-ground campaigns. However, these activities might imply high costs, risk and time consumption. Some more recent works have started to use magnetometers on-board remote piloted aircrafts. Normally, they fly at a constant altitude and use scalar probes. This configuration permits the determination of the magnitude of the magnetic field but not the direction, and requires advanced techniques for in-depth interpretation of the sources. In this manuscript, we describe the accommodation of a system for vector magnetometry in a drone whose flight altitude follows the elevation of the terrain. This singularity improves the capability of interpretation, including constraints in dating due to the record of the geomagnetic field. The work consists of the design, development and implementation of a solidary payload system anchored to the body of the platform in order to determine the vector magnetic field. It describes the details of the system and the performance characteristics obtained after the calibration, as well as its demonstration via a field campaign in the spatter deposits of Cerro Gordo volcano in Campos de Calatrava volcanic province in Spain. [ABSTRACT FROM AUTHOR]