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The demand for improved efficiency, the desire for increased power density and stabilization of material pricing have resulted in a more widespread application of permanent magnet motors. Once restricted to use on certain specialized applications, permanent magnet motors are now being applied on a large variety of equipment including drilling rigs, process pumps, cooling towers, etc. As the original “A MOTOR PRIMER” papers [1]–[9] did for induction motors, there is a need to educate the user community on the operation and application of permanent magnet motors. Topics discussed include permanent magnet materials, common rotor configurations, application guidelines and serviceability. This paper is meant to serve as a reference for those who specify, apply, operate or service these types of machines.

With increasing demand for higher efficiency, engineers are looking for an alternative solution to replace induction motors (IMs) in many applications. One of the very promising topologies is the permanent magnet (PM) motor with a squirrel cage on the rotor, which is referred to as the cage rotor PM motor in this paper. Based on the literature survey, the cage rotor PM motors have been studied and compared to IMs for direct on-line applications. Some studies also investigated the performance of line start PM motors (LSPMs) using a conventional VSD by comparing it to an IM using the same VSD. The comparative evaluation shows that, in most operating conditions, the LSPM drive provides better performance (usually higher efficiency) than the IM drive. In this paper, a d-q dynamic analytical model of the cage rotor PM motor is derived. The model is intended to be used in place of finite element analysis (FEA) and for purposes of control development. The model is implemented in Matlab/Simulink and used to study the behavior of different types of the cage rotor PM motor; line-start PM and light cage PM motors, when fed by a standard, scalar controlled drive. The analytical results are verified against the FEA, which shows excellent agreement.

This study investigates the concept, analysis, and design of the Permanent Magnet (PM) Trans-Rotary Magnetic Gear (TROMAG). The device converts linear motion to rotation and vice-versa through the magnetic field, and does the gearing action at the same time. It can convert the low speed high force linear motion of a translator to the low torque high speed rotation of a rotor; a promising capability for wave energy applications in particular. The structure of the TROMAG is presented, along with results of three-dimensional (3D) Finite Element Analysis (FEA), as well as the basic tenets of continuous force transmission in orthogonal directions by the elementary magnetic arrays. Moreover, a simple analytical method is set forth for calculating the force of the device. The method is based on the replacement of a PM by an equivalent current carrying coil and the electromagnetic image method theory. In addition, design and scaling of the TROMAG is discussed and the impact of several design parameters on the performance of the TROMAG is investigated through an accurate analytical model. Finally, transient performance of the TROMAG is discussed

Different design possibilities for passive suppression of transient oscillations in magnetic gears are investigated. Compared to their mechanical counterparts magnetic gears enjoy the advantage of having inherent overload capability, yet they suffer from a much lower torsional stiffness, resulting in long-lasting oscillations during transient changes in speed and load. Damper windings have long been used in synchronous generators to suppress oscillations due to transients, and are applied here to the magnetic gear for the same purpose. The effect of different design parameters such as location and number of bars, as well as their dimensions, and resistance of the end ring on the performance of damper windings is investigated. The possibility of inserting damper bars in the free space between the stator pole pieces is also inspected.

This paper investigates the effectiveness of damper windings for improving the transient behavior of magnetic gears. Despite their inherent overload capability, which is a great merit compared to mechanical gears, magnetic gears suffer from a low torsional stiffness and damping coefficient. This leads to long lasting oscillations during the transient state succeeding a change in speed or load. Damper windings have long served as a passive solution to the suppression of transient oscillations in synchronous machines. In this investigation, the same idea has been successfully applied to magnetic gears to achieve a gear with enormously improved transient suppression capability.

Soft cosmic ray tomography has been shown to successfully discriminate materials with various density levels due to their ability to deeply penetrate matter, allowing sensitivity to atomic number, radiation length and density. Because the multiple muon scattering signal from high Z-materials is very strong, the technology is well suited to the detection of the illicit transportation of special and radiololgical nuclear materials. In addition, a recent detection technique based on measuring the lower energy particles that do not traverse the material (range radiography), allows to discriminate low and medium Z-materials. We have demonstrated it first using Monte Carlo simulations. More recently, using a Mini-Muon Tracker developed at Los Alamos National Laboratory, we performed various experiments to try out the radiation length technology. This paper presents the results from real experiments and evaluates the likelihood that soft cosmic ray tomography may be applied to detect high-explosives.

Magnetic gears have been proposed as a means of increasing torque density within electromechanical systems, while avoiding problems associated with traditional mechanical gears. While the idea behind magnetic gears goes back to early patents, their study and use in industry has been very limited to date. This study looks into variations of the concentric planetary magnetic gear which could lead to further use. It also investigates applying Winding Function Theory (WFT) to aid in analysis of the gear. The new application of winding function theory is verified with the use of Finite Element Analysis (FEA) as well as a prototype which has been fabricated.

Charged particle tomography has been gaining ground due to its unique probing characteristics, and the ability of cosmic ray muons to deeply penetrate matter, allowing sensitivity to atomic number, radiation length and material density via detection of the probing particles before and after target traversal. But not all particles completely traverse the target volume. This paper explores the usefulness and information content in the stopped particles, referred to as soft particles. We show, in simulations, that the stopped particles are sensitive to low Z material and therefore provides complementary information to the scattering data.

The rising popularity of mobile devices, such as cellular phones and PDAs, has made them a lucrative playground for mobile malware propagation. One common infection vector exploited by these mobile malware is Bluetooth. In this paper, we propose an architecture called Blue-Watchdog that detects Bluetooth worm propagation in public areas based on statistical methods. To achieve fast and accurate Bluetooth worm detection, Blue-Watchdog monitors abrupt changes of average paging rate per Bluetooth device from both temporal and temporal-spatial perspectives. The temporal scheme relies on the CUSUM (Cumulative Sum) sequential test together with the generalized likelihood ratio (GLR), and the temporal-spatial scheme aims to identify spatial regions with abnormally frequent paging attempts. Experimental results show that Blue-Watchdog not only has low false alarm rates, but also effectively detects Bluetooth worms that spread quickly in areas where Bluetooth devices are greatly mixed due to high mobility and also those that propagate relatively slowly in a spatially constrained fashion.

This paper presents a a general framework for applying individual decision models to aggregated populations. Our approach is useful for modeling and predicting evacuation decisions from disasters, ranging from earthquakes, flooding and wild fires, to industrial emergencies like chemical spills or nuclear accidents, to reactions to terrorism attacks. The novelty of our approach is to apply well-documented household evacuation behavioral models to statistical accurate synthetic populations with detailed demographic information. Predictions of who evacuates and who does not evacuate, and the type and location of the selected shelters is useful for emergency management and planning. We illustrate it by applying our tools to predict emergency relocation behavior from hurricanes.

The wind turbine industry is growing at an incredible rate as numerous governments demand an increase in renewable energy generation capacity. The great multitude of turbines on windfarms throughout the world range from ones which are controlled mechanically with gears to ones which are controlled with power electronics and no gears. A drivetrain for a wind turbine which eliminates the problems of a mechanical gearbox and avoids the size and weight issues of directly-driven wind turbines is desired. A magnetic gear is proposed to reduce the size of the generator and achieve similar torque reduction provided by a mechanical gearbox, without the maintenance and breakdown issues. A magnetic gearbox, of the concentric planetary type will be studied for the high-torque, low-speed requirements of a wind turbine hub. Torque ripple is investigated across multiple models to determine acceptable torque transfer performance.

Marine propulsion systems have become increasingly electromechanical in recent years. Proposed systems show increasing torque density in an effort to reduce volume and weight. A magnetic gear is proposed to reduce the size of the propulsion motor and achieve similar torque amplification provided by a mechanical gearbox, without the maintenance and breakdown issues. A magnetic gearbox, of the concentric planetary type, will be studied for the high-torque low-speed requirements of a marine propulsion system. Torque ripple is investigated across multiple models to determine acceptable torque transfer performance.

We revisit the problem of computing the path with the minimum cost in terms of the expected number of link layer transmissions (including retransmissions) in wireless mesh networks. Unlike previous efforts, such as the popular ETX, we account for the fact that MAC protocols (including the IEEE 802.11 MAC) incorporate a finite number of transmission attempts per packet. This in turn leads to our key observation: the performance of a path depends not only on the number of the links on the path and the quality of its links, but also, on the relative positions of the links on the path. Based on this observation, we propose ETOP, a path metric that accurately captures the expected number of link layer transmissions required for reliable end-to-end packet delivery. We analytically compute ETOP, which is not trivial, since ETOP is a noncommutative function of the link success probabilities. Although ETOP is a more involved metric, we show that the problem of computing paths with the minimum ETOP cost can be solved by a greedy algorithm. We implement and evaluate a routing approach based on ETOP on a 25-node indoor mesh network. Our experiments show that the path selection with ETOP consistently results in superior TCP goodput (by over 50% in many cases) compared to path selection based on ETX. We also perform an in-depth analysis of the measurements to better understand why the paths selected by ETOP improve the TCP performance.

In this paper, we present queue management algorithms with low implementation complexity that can partition the bandwidth of an outgoing link among flows in a high speed packet switch. These algorithms belong to a family of queue management schemes called sending rate estimate based queue management schemes (SREQM), which try to prevent congestion by effectively limiting flows based on their estimated sending rates. We also present techniques based on sampling and Bloom filters to further reduce the implementation overhead. The capability of the algorithms to protect responsive flows from non-responsive flows are confirmed by exhaustive analysis and simulations.

We propose a rigorous mathematical interpretation of a novel family of active queue management schemes, called sending rate estimate based queue management (SREQM) scheme, that aims to provide fair bandwidth allocation to all the flows in a router by estimating the flow sending rates, while maintaining only minimal per-flow state information. We propose an optimized implementation of SREQM, called fair sending rate estimate based queue management (FSREQM) scheme, and show through comparative simulation that FRESQM is the only scheme among those tested that successfully prevents bandwidth abuse while maintaining high link utilization.

The paths of muons traveling through matter are affected by Coulomb multiple scattering. The magnitude of that interaction depends on the radiation length of the traversed medium, with high-Z matter scattering more than low-Z matter. The net effect on the path of a muon through an object is both a change in the direction and an orthogonal displacement orthogonal. Both these quantities can be measured experimentally for individual muons, opening the possibility of tomographic reconstruction of the radiation lengths of unknown complex objects. We present a framework to characterize the ability to reconstruct the scattering density from muon scattering data for detecting and locating areas of high Z material. Our analysis shows the importance of having detectors with large aperture, and the importance of using both the change in angle and displacement

We have built a detector capable of locating high Z objects in the sampling (middle) region of the detector. As atomic number increases, radiation length rapidly decreases, yielding larger variance in scattering angle. Cosmic ray muon tomography works by tracking muons above the sampling region, and tracking them below the region as well. The difference between the two trajectories yield information, via the muon scattering variance, of the materials contained within the sampling region [Borozdin, K, et al., 2003]. One of most important aspects of cosmic ray tomography is minimizing exposure time. The cosmic ray flux is about 1 cm-2 min-1, and the goal is to use them for detecting high-density materials as quickly as possible. This involves using all of the information possible to reconstruct tracks with redundant detectors. Detector scattering residuals yield a low precision measurement of muon energy. Knowing the rough energy of an incoming particle will yield more precisely the expected scattering variance (currently the expectation value of ~3 GeV is used).

In many applications the time available for nuclear materials detection is short, the signals are weak, and the backgrounds are large, variable, and difficult to control. We develop optimal strategies for nuclear search in these difficult conditions, and demonstrate these strategies in experiments. Model-driven algorithms promise to reduce search time by order of magnitude, with increased reliability of detection and reduced number of false positives. These strategies can be applied to various nuclear search scenarios, for both mobile and stationary detectors, hand-held detectors and sensors on robotic platforms. Our methods can be used against nuclear smugglers and terrorists, for safeguards and non-proliferation treaty monitoring, as well as in other situations where radioactive sources need to be found.

We present a technique for combining prior knowledge about transformations that should be ignored with a covariance matrix estimated from training data to make an improved Mahalanobis distance classifier. Modern classification problems often involve objects represented by high-dimensional vectors or images (for example, sampled speech or human faces). The complex statistical structure of these representations is often difficult to infer from the relatively limited training data sets that are available in practice. Thus, we wish to efficiently utilize any available a priori information, such as transformations or the representations with respect to which the associated objects are known to retain the same classification (for example, spatial shifts of an image of a handwritten digit do not alter the identity of the digit). These transformations, which are often relatively simple in the space of the underlying objects, are usually nonlinear in the space of the object representation, making their inclusion within the framework of a standard statistical classifier difficult. Motivated by prior work of Simard et al. (1998; 2000), we have constructed a new classifier which combines statistical information from training data and linear approximations to known invariance transformations. When tested on a face recognition task, performance was found to exceed by a significant margin that of the best algorithm in a reference software distribution.