基于分形的水声图像目标探测

针对水声图像中人造物体的探测问题，给出了一种基于分形分析的方法，由于分形模型可以较好地模拟自然物体，而与人工物体存在较大差距，所以以其为主要特征可以准确地将人造物体从自然背景中探测出来。本文讨论了分维的提取方法，根据分形特征将水声图像标记为人造目标区域和非人造目标区域，并对一定噪声干扰下该方法的应用进行了研究，给出了相应的实验结果。实验结果表明，分形特征可以实现人造目标和自然物体的分类，并具有一定的抗噪声性，适宜对水声图像中的目标进行探测和识别。     

广义模糊Gibbs随机场与MR图像分割算法研究

为了对图像进行准确、可靠的分割,提出了一种基于广义模糊集的软分割算法,并将广义模糊集和G ibbs场结合起来,提出了广义模糊G ibbs随机场模型,同时建立了广义模糊G ibbs分割(GFGS)算法。该算法是首先把每一个分割类看作是广义模糊类,并以最大后验概率(MAP)为判别准则来决定每一个像素值的归类以及它属于该类的隶属度;然后用广义隶属度函数中负的部分来刻划数据中的异常值,使得该算法能有效地处理异常值;最后用该模糊类的质心来更新类的中心,并以人脑的仿真图像和临床MR图像进行了实验。实验结果表明,该算法能有效地滤除噪声和处理部分容积效应,是一个分割能力强、稳健性好的算法。     

Computational Issues in the Sequential Probit Model: A Monte Carlo Study

We discuss computational issues in the sequential probit model that have limited its use in applied research. We estimate parameters of the model by the method of simulated maximum likelihood (SML) and by Bayesian MCMC algorithms. We provide Monte Carlo evidence on the relative performance of both estimators and find that the SML procedure computes standard errors of the estimated correlation coefficients that are less reliable. Given the numerical difficulties associated with the estimation procedures, we advise the applied researcher to use both the stochastic optimization algorithm in the Simulated Maximum Likelihood approach and the Bayesian MCMC algorithm to check the compatibility of the results. JEL Classifications: C11, C15, C35, C63     

The thermal capacity and enthalpy of gadolinium sesquiselenide over a wide temperature range

Measurements have been made on the thermal capacity of γ-Gd₂Se₃ at 58.88–298.34 K. Values have been obtained for the thermal capacity, entropy, reduced Gibbs energy, and enthalpy under standard conditions: C°_p = 125.87 ± 0.5 J· mole⁻¹ · K⁻¹; S°(298.15 K) = 196.5 · 1.6 J · mole⁻¹ · K⁻¹; Φ°(298.15 K) = 103.6 ± 1.6 J · mole⁻¹ · K⁻¹; H°(298.15 K)-H°(0) = 27681 ± 138 J · mole⁻¹. The enthalpy of Gd₂Se₃ has been measured and the major thermodynamic functions have been calculated for the solid and liquid states over the temperature range 450–2300 K. The temperature dependence of the enthalpy in the ranges 300–1800 K and 2000–2300 K are represented: H°(T)-H°(298.15 K) = = 1.1949 · 10⁻² · T² + 122.38 · T + 347402 · T⁻¹ − 38716 and H°(T)-H°(298.15 K) = 262.81 · T-− 196047, respectively. The calculated temperature, enthalpy, and entropy of melting for Gd₂Se₃ are: T_m = 1925 ± 40 K, Δ_mH° (Gd₂Se₃) = 68.5 kJ · mole^-1, Δ_mS°(Gd₂Se₃) = 35.6 J · mole⁻¹ · K⁻¹. __________ Translated from Poroshkovaya Metallurgiya, Nos. 3–4(448), pp. 56–61, March–April, 2006.     

Using duration models to reduce fragmentation in audio segmentation

We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Markov Chain Monte Carlo methods; in particular, we introduce a modification to Wolff’s algorithm to make it applicable to a segment classification model with an arbitrary duration prior. We apply this to a collection of pop songs, and show experimentally that the generated segmentations suffer much less from fragmentation than those produced by segmentation algorithms based on clustering, and are closer to an expert listener’s annotations, as evaluated by two different performance measures. Editor: Gerhard Widmer     

Standard Gibbs Energies of Transfer for KBPh4 from Water to Water ＋ PrOH and Water ＋ TBA Mixtures and the Primary Medium Effects

The standard Gibbs energiics of transfer (△rG^0) for potassium tetraphenylborate (KBPh4) have been studied in the system of water and water l-propamol (PrOH) as well as water and water t-butyl alcohol (TBA) at 298.15K. The rcsults show that -△rG^0 cxhibits a complicated changing pattern with the mole fraction of TBA(x (TBA)) or PrOH (x (PrOH)), and △rG^0 has the a maximum value at x (TBA) = 0.2 or x (PrOH) = 0.2. Especially, -△rG^0 of KBPh4 changcs unusually with increasing x (TBA) when x (TBA) ＜0.05. The reasons for these changes were analyzed and discussed.     

A Stochastic Neural Model for Fast Identification of Spatiotemporal Sequences

In this Letter, a new approach to build a neural model for the fast identification of spatiotemporal sequences is proposed. Such a model, the Stochastic Neural Sequence Identifier (SNSI), is simple and rapidly learns and identifies a given sequence. The SNSI receives as input several patterns belonging to a particular spatiotemporal sequence and produces as output a label for the sequence identified and a probability of this classification being correct. The SNSI is able to identify a sequence from patterns learned during training or novel ones, i.e., combinations of the sequence items distinct from those belonging to the trained set. The SNSI was tested on a 2D set of both closed and open trajectories with varying levels of complexity. The results suggest that the SNSI is able to recognize all the patterns presented in the training and most of the novel patterns used for testing.     

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.     

PAC-Bayesian Stochastic Model Selection

PAC-Bayesian learning methods combine the informative priors of Bayesian methods with distribution-free PAC guarantees. Stochastic model selection predicts a class label by stochastically sampling a classifier according to a posterior distribution on classifiers. This paper gives a PAC-Bayesian performance guarantee for stochastic model selection that is superior to analogous guarantees for deterministic model selection. The guarantee is stated in terms of the training error of the stochastic classifier and the KL-divergence of the posterior from the prior. It is shown that the posterior optimizing the performance guarantee is a Gibbs distribution. Simpler posterior distributions are also derived that have nearly optimal performance guarantees.     

Viscosity of Some Binary Mixtures of Arenes

The viscosity of 12 binary mixtures of benzene+toluene, +ethylbenzene, +isopropylbenzene, +tert-butylbenzene; toluene+ethylbenzene, +isopro- pylbenzene, +tert-butylbenzene; ethylbenzene+isopropylbenzene; isopropylbenzene+tert-butylbenzene; o-xylene+m-xylene; m-xylene+p-xylene; and p-xylene+o-xylene has been measured over the entire range of composition. The viscosity deviations and excess Gibbs energy of activation G ^*E of viscous flow based on Eyring's theory have been calculated. The results have been analyzed in terms of the change in the structure of pure component molecules. The viscosity data have been correlated with the equations of Grunberg and Nissan; Hind, McLaughlin, and Ubbelohde; Tamura and Kurata; Katti and Chaudhri; McAllister; and Heric and Brewer. The Prigogine–Flory–Patterson– Bloomfield–Dewan (PFPBD) theory has been applied to analyze the excess viscosity of the present binary mixtures.