Comparison of the MMPI and the MMPI-2 for a sample of peace officers.

166 peace officers completed the MMPI and the MMPI-2 in one sitting. Results showed an overall concordance of 78% for the 2 tests when normal, high-point, and 2-point code types were grouped together and compared. A subset of well-defined profiles produced a concordance rate of 90%. Comparison of profile characteristics showed that half of the Ss produced the same high-point code type, one-third produced the same 2-point code type, 70% produced normal profiles on both tests, and all MMPI and MMPI-2 scales were highly correlated. These figures are highly similar to those found previously for 2 administrations of the MMPI. When the MMPI was compared to the MMPI-2 scored on original norms, 2 scales were found to differ significantly: Men and women both scored lower on Scale D (Depression) of the revised test; on Scale Mf (Masculinity-Femininity) of the MMPI-2, women scored higher and men scored lower. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Note: a high transmission Faraday optical isolator in the 9.2 μm range

We have fabricated and characterized an n-doped InSb Faraday isolator in the mid-IR range (9.2 μm). A high isolation ratio (31(2) dB) and low insertion loss (1.9(3) dB) are obtained. Temperature dependance is analyzed. Further possible improvements are discussed, including the realization of a two-stage isolator. A similar design can be used to cover a wide wavelength range (λ ~ 7.5-30 μm).     

Does code decay? Assessing the evidence from change management data

A central feature of the evolution of large software systems is that change-which is necessary to add new functionality, accommodate new hardware, and repair faults-becomes increasingly difficult over time. We approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed     

A Self-Tuning Evolutionary Algorithm Applied to an Inverse Partial Differential Equation

Evolutionary algorithms (EAs) are becoming increasingly popular tools for solving complex search problems. Their popularity in various problem domains has led to the introduction and development of numerous variants of two standard EA operators—crossover and mutation. Unfortunately, there are few if any effective guidelines for choosing which operators will be most effective in a given problem. In this paper, a self-tuning EA is introduced that employs several crossover and mutation operators simultaneously. The probability of using a given operator changes during the course of an evolutionary run whereby the most effective operators are selected based on which part of the search space is currently being explored. The self-tuning EA is used to solve an inverse partial differential equation—considered to be one of the more difficult problems in the realm of engineering mathematics. Results indicate that for the particular inverse partial differential equation considered, the self-tuning EA provides an effective solution methodology.     

Reactions of iron sulphides in simulated stack gases

The rates of production and consumption of SO₂ by the reactions of O₂, SO₂, and mixtures of O₂ and SO₂ in simulated stack gases with fixed and fluidised beds of FeS prepared from pyrite were determined over the temperature range 800 to 950 °C. The rates of production of SO₂ by reaction of Fe₂O₃ with FeS in different gas systems were also determined. The rate controlling factors were derived for each reaction.     

SR-30 turbojet engine real-time sensor health monitoring using neural networks,and Bayesian belief networks

This paper describes the use of artificial intelligence-based techniques for detecting and isolating sensor failures in a turbojet engine. Specifically, three artificial intelligence (AI) techniques are employed: artificial neural networks (NNs), statistical expectations, and Bayesian belief networks (BBNs). These techniques are combined into an overall system that is capable of distinguishing between sensor failure and engine failure—a critical capability in the operation of turbojet engines. The turbojet engine used in this study is an SR-30 developed by Turbine Technologies. Initially, NNs were designed and trained to recognize sensor failure in the engine. The increased random noise output from failing sensors was used as the key indicator. Next, a Bayesian statistical method was used to recognize sensor failure based on the bias error occurring in the sensors. Finally, a BBN was developed to interpret the results of the NN and statistical evaluations. The BBN determines whether single or multiple sensor failures signify engine failure, or whether sensor failures represent separate, unrelated incidences. The BBN algorithm is also used to distinguish between bias and noise errors on sensors used to monitor turbojet performance. The overall system is demonstrated to work equally well during start-up and main-stage operation of the engine. Results show that the method can efficiently detect and isolate single or multiple sensor failures within this dynamic environment.     

Atmospheric Phase Error in Coherent Laser Radar

Coherent laser radar (ladar) deduces the shape, velocity and vibration of a remote target by measuring the round-trip optical phase or the Doppler shift of target-scattered light. Phase fluctuations from wind-driven atmospheric turbulence generate micro-Doppler or velocity error at mum/sec levels. The turbulence-induced noise spectra of all geometric phase components of the ladar, the average phase power spectral densities of arbitrary ladar receiver modes, and the resultant Doppler noise spectra, are derived in the Rytov approximation for any isotropic stationary turbulence, any wind velocity profile, any Fresnel number and any source illumination pattern. In Kolmogorov turbulence the piston phase noise spectrum, and the average phase shift over a time interval, are well-approximated by simple universal asymptotic forms dependent only on atmospheric coherence length, turbulence-weighted average wind speed Vmacr, and aperture size D. The spectrum slope changes at a critical frequency of order Vmacr/D. The approximate results do not depend on the usually-unknown outer scale of turbulence     

EXPERIMENTAL IMPLEMENTATION OF AN ARTIFICIAL IMMUNE SYSTEM ON A ROBOTIC MANIPULATOR

An artificial immune system (AIS) has been used to realize robust control of a robotic manipulator. The AIS recognizes “self” and “non-self” operation of a closed-loop system, where self is defined as a condition where controller gains are appropriate for a given manipulator configuration. As configuration changes occur, the changing performance of the system indicates a transition to non-self. When non-self operation is first detected, the corresponding dynamic response is defined as a receptor and a genetic algorithm (GA) is called to optimize the controller for the new configuration. A library of receptors is built as additional configuration changes are experienced. For subsequent self to non-self transitions, new and recorded receptors are compared. In the event of a high correlation between the receptors, previously determined controller gains are implemented without calling the GA. The system is agile and robust and can recognize and respond to recognized receptors within a single reference step.