Imaging the solar corona in the EUV

The SOHO (SOlar and Heliospheric Observatory) satellite was launched on December 2nd 1995. After arriving at the Earth-Sun (L1) Lagrangian point on February 14th 1996, it began to continuously observe the Sun. As one of the instruments onboard SOHO, the EIT (Extreme ultraviolet Imaging Telescope) images the Sun's corona in 4 EUV wavelengths. The He II filter at 304 Å images the chromosphere and the base of the transition region at a temperature of 5 − 8 × 10⁴ K; the Fe IX–X filter at 171 Å images the corona at a temperature of 1.3 × 10⁶ K; the Fe XII filter at 195 Å images the quiet corona outside coronal holes at a temperature of 1.6 × 10⁶ K; and the Fe XV filter at 284 Å images active regions with a temperature of 2.0 × 10⁶ K. About 5000 images have been obtained up to the present. In this paper, we describe also some aspects of the telescope and the detector performance for application in the observations. Images and movies of all the wavelengths allow a look at different phenomena present in the Sun's corona, and in particular, magnetic field reconnection.     

UVCS/SOHO Observations of Spectral Line Profiles in Polar Coronal Holes

Ultraviolet emission line profiles have been measured on 15-29 September 1997 for H I 1216 Å, O VI 1032, 1037 Å and Mg X 625 Å in a polar coronal hole, at heliographic heights ? (in solar radii) between 1.34 and 2.0. Observations of H I 1216 Å and the O VI doublet from January 1997 for ? = 1.5 to 3.0 are provided for comparison. Mg X 625 Å is observed to have a narrow component at ? = 1.34 which accounts for only a small fraction of the observed spectral radiance, and a broad component that exists at all observed heights. The widths of O VI broad components are only slightly larger than those for H I at ? = 1.34, but are significantly larger at ? = 1.5 and much larger for ? > 1.75. In contrast, the Mg X values are less than those of H I up to 1.75 and then increase rapidly up to at least ? = 2.0, but never reach the values of O VI.     

Parametric models for helicopter identification using ANN

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal, ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated. It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise.     

基于市场的最适宜质量水平模型

以企业利润最大化为目标,引入效用的概念,提出了一个基于市场的最适宜质量水平模型。该模型突破了现有的质量经济学理论仅以质量成本确定最适宜质量水平的模式,将质量水平与产品成本,效用和市场绩效联系起来,使模型具有更为方法的适用性,本文还就市场不确定性,消费者偏好,技术进步,市场竞争等因素对模型的影响展开了讨论,揭示了质量,销量,价格,成本,技术与竞争之间的关系,为企业产品决策,技术改造决策和质量策划提供了参考依据。     

Evaluation of upper body muscle activity during cardiopulmonary resuscitation performance in simulated microgravity

Performance of efficient single-person cardiopulmonary resuscitation (CPR) is vital to maintain cardiac and cerebral perfusion during the 2–4 min it takes for deployment of advanced life support during a space mission. The aim of the present study was to investigate potential differences in upper body muscle activity during CPR performance at terrestrial gravity (+1Gz) and in simulated microgravity (μG). Muscle activity of the triceps brachii, erector spinae, rectus abdominis and pectoralis major was measured via superficial electromyography in 20 healthy male volunteers. Four sets of 30 external chest compressions (ECCs) were performed on a mannequin. Microgravity was simulated using a body suspension device and harness; the Evetts–Russomano (ER) method was adopted for CPR performance in simulated microgravity. Heart rate and perceived exertion via Borg scores were also measured. While a significantly lower depth of ECCs was observed in simulated microgravity, compared with +1Gz, it was still within the target range of 40–50 mm. There was a 7.7% decrease of the mean (±SEM) ECC depth from 48 ± 0.3 mm at +1Gz, to 44.3 ± 0.5 mm during microgravity simulation (p < 0.001). No significant difference in number or rate of compressions was found between the two conditions. Heart rate displayed a significantly larger increase during CPR in simulated microgravity than at +1Gz, the former presenting a mean (±SEM) of 23.6 ± 2.91 bpm and the latter, 76.6 ± 3.8 bpm (p < 0.001). Borg scores were 70% higher post-microgravity compressions (17 ± 1) than post +1Gz compressions (10 ± 1) (p < 0.001). Intermuscular comparisons showed the triceps brachii to have significantly lower muscle activity than each of the other three tested muscles, in both +1Gz and microgravity. As shown by greater Borg scores and heart rate increases, CPR performance in simulated microgravity is more fatiguing than at +1Gz. Nevertheless, no significant difference in muscle activity between conditions was found, a result that is favourable for astronauts, given the inevitable muscular and cardiovascular deconditioning that occurs during space travel.     

Fault Detection in Dynamic Systems via Decision Fusion

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques for dynamic systems are desired. In this paper, we consider fault detection in dynamic systems monitored by multiple sensors. Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent and identically distributed (1ID) observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on state estimation via particle filtering. Illustrative examples are presented to demonstrate the effectiveness of our approach.