导线平差取权探讨

导线平差中目前采用的取权方法有两个缺点:其一,只考虑测角和测边各自的精度比,而忽略了它们之间精度的互比;其二,未顾及测角和测边精度对导线计算的综合影响。因而导致了平差后角度改正值具有明显的系统性质。本文首先对经典取权法进行分析,指出其缺点,而后提出一组新的取权公式。最后以等边直伸导线为例导出其平差公式,并举例说明新法取权的优点。     

Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm (τ₀₅₅). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60-70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.     

状态观测器在故障诊断中的应用

伦伯格(D.G. Luenberger)的状态观测器理论在自动控制系统的设计中已得到广泛应用,但在故障诊断方面的应用尚不多见。本文引用状态观测器理论解决控制系统的故障诊断问题,对于不考虑随机噪声的线性或拟线性系统的故障可以作出比较准确的诊断。     

Robust trend parameters in a multivariate spatial linear model

This article gives a robust estimator of the trend parameters in multivariate spatial linear models. This estimator is presented as an alternative to the classical one which is obtained by using cokriging. The goal focuses on improving predictions of spatial variables when data contain both atypical and high influence observations. The procedure consists of extending robust methods used in linear regression models to the multivariate spatial context. The resulting estimator belongs to the class of GM-estimators and then, it is a bounded influence estimator and it has good robust properties, in particular, a high breakdown point and a high efficiency. An illustrative example is given to show how the proposed estimator works. Research partially supported by Ministerio de Ciencia y Tecnología, Project AGL2000-0978.     

An evolutionary algorithm for abductive reasoning

Abductive reasoning (or abduction) is the process of inferring hypotheses from observed data using a certain ‘knowledge’ encoded in the form of inference rules (or causal relations). Many important kinds of intellectual tasks, including medical diagnosis, fault diagnosis, scientific discovery, legal reasoning, and natural language understanding have been characterised as abduction. Unfortunately, abduction is 𝒩𝒫-hard. Genetic algorithms and biologically motivated computational paradigms inspired by the natural evolution turned out to be efficient in solving many hard problems while other existing approaches failed to solve in general. In this article, we present a genetic algorithm called HAKIM, for solving abduction problems. We encode an explanation in a chromosome-like structure, where every gene models a possible single hypothesis. Thereafter, we develop a fitness function that characterises the overall ‘quality’ of a chromosome representing an explanation; and then use standard genetic operators to compute a set of hypotheses that best explains the observed data. Simulation results on large-scale medical problems reveal the good performance of our model HAKIM.     

Assessing Counter-Terrorism field training with multiple behavioral measures

Development of behavioral pattern recognition and analysis skills is an essential element of Counter-Terrorism training, particularly in the field. Three classes of behavioral measures were collected in an assessment of skill acquisition during a US Joint Forces Command-sponsored course consisting of Combat Tracking and Combat Profiling segments. Measures included situational judgment tests, structured behavioral observation checklists, and qualitative assessments of the emergence of specific knowledge–skills–attitudes over the course of the training. The paper describes statistical evidence across the three types of measures that indicate that behavioral pattern recognition and analysis skills were successfully acquired by most students (a mix of Army and civilian law enforcement personnel) during the field training exercises. Implications for broader training of these critical skills are also discussed.     

Blind system identification using precise and quantized observations

This paper studies the blind identification of multi-channel FIR systems using precise and quantized observations. First, a new deterministic blind identification (DBI) algorithm is presented for multi-channel FIR systems using precise observations, in which the system parameters can be consistently estimated and the common source signal can be stably recovered. When the observed samples are quantized by a static finite-level quantizer, an iterative deterministic blind identification (IDBI) method is then provided. The asymptotic characters of the proposed IDBI method are discussed and the quantization effect on the identification performance is analyzed. Numerical simulations are given to support the developed DBI method and IDBI method.     

Influence of Assimilating the Sea Surface Pressure Fields Retrieved from FY-3C/MWHTS Data on Typhoon Forecasting

Using measurements with the Microwave Temperature and Humidity Sounder (MWHTS) onboard the Chinese Fengyun-3C satellite, real-time and high resolution sea surface pressure information can be retrived. Based on the three-dimensional variational assimilation (3DVAR) method, the retrieved sea pressure fields from FY-3C/MWHTS observations are assimilated into the Weather Research and Forecasting (WRF) model. The influence of the retrieved pressure fields on typhoon forecasting is discussed through the comparison between control experiment and assimilation experiment. Sensitivity experiments of typhoon Maria and Noru show that the assimilation of sea surface pressure fields makes the central pressure and central location closer to the actual value, and adjusts the structure and distribution of initial temperature fields and wind fields. The numerical prediction results show that the assimilation of the sea surface pressure fields can improve the accuracy of typhoon track and intensity prediction.     

Development of quantitative reasoning and gender biases.

One hundred twenty-seven 7-, 9-, and 11-year-old children were presented large or small samples of own-gender enhancing or other-gender enhancing observations. Children read arguments based on the observations, rated argument intelligence, judged the number of other children to whom the observations could be generalized, and provided verbal justifications for their judgments. Own-gender reasoning biases declined with age; these declines were, however, partially accounted for by declines in the strength of self-reported gender affiliations. Reasoning biases-demonstrated by problem-to-problem shifts in reasoning quality-were constrained by sample size, indicating a modest degree of rationality even among 7-year-olds. Specifically, biases co-existed with reasonably extensive generalization from large samples of other-gender evidence. Children were thus able to satisfy motivations for own-gender favoritism and reason in accord with the law of large numbers. Several explanations of the findings-based on changes in the salience of gender, multiple classification skills, and the ability to reason independently from beliefs-are offered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)