In the theory of blast waves

The behavior of the blast wave from an exploding spherical volume is investigated mathematically over the entire range of blast-wave propagation. The computational method uses analytical results for a similar problem for a point explosion with counter pressure and the theory of an asymptotically equivalent point explosion. The asymptotic solution of the gas-dynamic equations away from the explosion site is matched to the initial conditions of blast-wave formation due to the disintegration of the detonation shock upon its arrival at the boundary of the exploding volume. The spatial distribution of the blast-front pressure is found for combustible gaseous systems and solid explosives. The theoretical results obtained agree quantitatively with available experimental measurements. __________ Translated from Fizika Goreniya i Vzryva, Vol. 42, No. 4, pp. 93–99, July–August, 2006.     

Comparative analysis of urban reflectance and surface temperature

Urban environmental conditions are strongly dependent on the biophysical properties and radiant thermal field of the land cover elements in the urban mosaic. Observations of urban reflectance and surface temperature provide valuable constraints on the physical properties that are determinants of mass and energy fluxes in the urban environment. Consistencies in the covariation of surface temperature with reflectance properties can be parameterized to represent characteristics of the surface energy flux associated with different land covers and physical conditions. Linear mixture models can accurately represent Landsat ETM+ reflectances as fractions of generic spectral endmembers that correspond to land surface materials with distinct physical properties. Modeling heterogeneous land cover as mixtures of rock and/or soil Substrate, Vegetation and non-reflective Dark surface (SVD) generic endmembers makes it possible to quantify the dependence of aggregate surface temperature on the relative abundance of each physical component of the land cover, thereby distinguishing the effects of vegetation abundance, soil exposure, albedo and shadowing. Comparing these covariations in a wide variety of urban settings and physical environments provides a more robust indication of the global variability in these parameter spaces than could be inferred from a single study area. A comparative analysis of 24 urban areas and their non-urban peripheries illustrates the variability in the urban thermal fields and its dependence on biophysical land surface components. Contrary to expectation, moderate resolution intra-urban variations in surface temperature are generally as large as regional surface heat island signatures in these urban areas. Many of the non-temperate urban areas did not have surface heat island signatures at all. However, the multivariate distributions of surface temperature and generic endmember fractions reveal consistent patterns of thermal fraction covariation resulting from land cover characteristics. The Thermal-Vegetation (TV) fraction space illustrates the considerable variability in the well-known inverse correlation between surface temperature and vegetation fraction at moderate (< 100 m) spatial resolutions. The Thermal-Substrate (TS) fraction space reveals energetic thresholds where competing effects of albedo, illumination and soil moisture determine the covariation of maximum and minimum temperature with illuminated substrate fraction. The dark surface endmember fraction represents a fundamental ambiguity in the radiance signal because it can correspond to either absorptive (e.g. low albedo asphalt), transmissive (e.g. deep clear water) or shadowed (e.g. tree canopy shadow) surfaces. However, in areas where dark surface composition can be inferred from spatial context, the different responses of these surfaces may still allow them to be distinguished in the thermal fraction space.     

SPECTRAL DENSITY ESTIMATION FROM NONLINEARLY OBSERVED DATA

Abstract. The estimation of the spectral density function of a stationary Gaussian process at the input of an instantaneous nonlinearity is considered when the nonlinearity is known and a finite set of observations of the output process is given. A class of spectral estimates is considered and their quadratic-mean consistency is established; precise asymptotic expressions for their bias and covariance are derived and their asymptotic normality is obtained.     

A frame-based knowledge representation model and its mapping to nested relations

The first part of this paper introduces the basic constructs of a frame representation model and gives a formal definition for them. Subsequently the nested relational model (NF²) is described as an extension of the classical relational model to support relation-valued attributes. In the second part of this paper both models are compared with each other and a mapping of frame representation structures to NF² structures is specified. The structural similarities between frames and NF² relations are made clear and it is shown that their main difference is due to the type polymorphism introduced by the concept hierarchy of the frame model. This causes type collisions to occur in the strictly typed NF² model when frames are directly mapped to NF² structures. Two solutions to this problem are suggested and compared. The paper concludes with a reformulation of query operations of the frame model in terms of NF² algebra operations.     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

论科技论文英语摘要的表现形式

论述科技论文中英语摘要的文体风格、时态、语态和基本要求，并结合例句介绍了常用的表现形式，使之简洁、准确和流畅。     

Orientation-based unique representation for planar curves and shapes

This paper presents an orientation-based representation for planar curves and shapes. The new representation can uniquely represent all types, of planar shapes, be it convex, nonconvex, polygonal, smoothly curved, or piecewise smooth shapes. It is based on a new parameterization, theabsolute integral orientation. This representation is invariant under translation and rotation. The absolute integral orientation is a parameter invariant under scaling. As a result, matching of similar shapes (i.e., determination of the relative orientation and the scaling factor) using the absolute integral orientation as the parameter is easier than using the arclength as the parameter. In addition, the new representation has the feature of adaptive sampling, making it more compact and efficient than arc-length-based representations.     

基于参考点的模糊知识表示

本文分析了模糊关系模型的知识表示途径，并由此提出基于参考点的模糊知识表示，分析了它与人类记忆经验性知识的相信性，并证明模糊关系模型能以任意精度逼近具有有限个极值点的有界连续函数。