The inspection of anisotropic single-crystal components using a 2-D ultrasonic array

Single-crystal metal alloys are used extensively in the manufacture of jet engine components for their excellent mechanical properties at elevated temperatures. The inspection of these components using 2-D ultrasonic arrays potentially allows the detection of subsurface defects in threedimensions from one inspection location. Such methods are not currently suitable for the inspection of single-crystal components because the high elastic anisotropy of single-crystal materials causes directional variation in ultrasonic waves. In this paper, a model of wave propagation in anisotropic material is used to correct an ultrasonic imaging algorithm and is applied to a single-crystal test specimen. For this correctedalgorithm, the orientation of the crystal in a specimen must be known before the inspection. Using the same ultrasonic array to measure the orientation and perform the defect inspection offers the most practical solution. Therefore, potential crystallographic orientation methods using 2-D ultrasonic arrays are also developed and evaluated.     

Phase retrieval from subdivision of the focal plane with a lenslet array

A phase retrieval algorithm derived from subdivision of the complex field at the focal plane is proposed. This subdivision is achieved with a lenslet array at the focal plane in a manner similar to the pyramid wave-front sensor. The phase retrieval algorithm significantly improves the wave-front estimate that can be attained as a linear combination of the aperture images. This phase retrieval algorithm also avoids the twin-image stagnation problem inherent in phase retrieval and phase retrieval in conjunction with the Shack-Hartmann sensor.     

Simplification of genetic programs: a literature survey

Data Mining and Knowledge Discovery - Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals’ sizes....     

Performance and reliability prediction for evolving service-oriented software systems

During software system evolution, software architects intuitively trade off the different architecture alternatives for their extra-functional properties, such as performance, maintainability, reliability, security, and usability. Researchers have proposed numerous model-driven prediction methods based on queuing networks or Petri nets, which claim to be more cost-effective and less error-prone than current practice. Practitioners are reluctant to apply these methods because of the unknown prediction accuracy and work effort. We have applied a novel model-driven prediction method called Q-ImPrESS on a large-scale process control system from ABB consisting of several million lines of code. This paper reports on the achieved performance prediction accuracy and reliability prediction sensitivity analyses as well as the effort in person hours for achieving these results.     

Space-efficient recognition of sparse self-reducible languages

Mahaney and others have shown that sparse self-reducible sets have time-efficient algorithms, and have concluded that it is unlikely that NP has sparse complete sets. Mahaney's work, intuition, and a 1978 conjecture of Hartmanis notwithstanding, nothing has been known about the density of complete sets for feasible classes until now. This paper shows that sparse self-reducible sets have space-efficient algorithms, and in many cases, even have time-space-efficient algorithms. We conclude that NL, NC ^k , AC ^k , LOG(DCFL), LOG(CFL), and P lack complete (or even Turing-hard) sets of low density unless implausible complexity class inclusions hold. In particular, if NL (respectively P, _k , or NP) has a polylog-sparse logspace-hard set, then NLSC (respectively PSC, _k , or PHSC), and if P has subpolynomially sparse logspace-hard sets, then PPSPACE.     

SVM practical industrial application for mechanical faults diagnostic

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine‘s vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM’s it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.     

Using Coplink to analyze criminal-justice data

The paper discusses the Coplink system. The system applies a concept space-a statistics-based, algorithmic technique that identifies relationships between suspects, victims, and other pertinent data-to accelerate criminal investigations and enhance law enforcement efforts. The Coplink concept space application, which began as a research project, has evolved into a real-time system being used in everyday police work. Coplink CS has been successfully deployed at the Tucson Police Department, where crime analysts, officers, detectives, and sergeants from 16 departmental units use the technology voluntarily as part of their daily investigative routine     

Electrostatic Deposition of Pesticide Sprays onto Ionizing Targets: Charge- and Mass-Transfer Analysis

Electrostatic deposition of charged pesticide sprays onto biological targets may be undesirably limited under certain operational conditions by gaseous-discharge currents induced to flow between the incoming charged spray cloud and grounded points on the target (e.g. leaf tips). Spray deposition and charge transfer onto idealized target models as functions of target characteristics and intensity of spray-droplet charging are quantified experimentally to achieve a more fundamental understanding of this phenomenon. As compared to similar uncharged sprays, deposition onto smooth spherical, and planar targets was increased up to seven-fold and three-fold, respectively, at optimum spray-charge levels. Space-charge induced target ionization from a 20-mm point protrustion self-limited this electrostatic deposition to only a 3.5-fold increase for the spherical target but caused little reduction for the planar target. As compared with a hydraulic-atomizing nozzle, the charging nozzle increased deposition approximately eight-fold and 24-fold, respectively, onto the spherical and the planar targets. The ratios of charge-to-mass collected on all targets were unexpectedly found to exceed values calculated for the airborne charged-droplet cloud, indicating a possible contact-discharge during elastic collision of conductive droplets at the target.