CHAOTIC HYDRODYNAMICS IN HORIZONTAL GAS-LIQUID BUBBLY FLOW

ＮＯＭＥＮＣＬＡＴＵＲＥｄ———ｅｍｂｅｄｄｉｎｇｄｉｍｅｎｓｉｏｎ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＣｄ(ｒ)———ｃｏｒｒｅｌａｔｉｏｎｓｕｍ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＮ———ｎｕｍｂｅｒｏｆｐａｉｒｓｏｆｐｏｉｎｔｓｏｎｔｈｅｒｅｃｏｎ ｓｔｒｕｃｔｅｄａｔｔｒａｃｔｏｒ,ｄｉｍｅｎｓｉｏｎｌｅｓｓｒ———ｓｃａｌｉｎｇｄｉｓｔａｎｃｅ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＤｃ ———ｃｏｒｒｅｌａｔｉｏｎｄｉｍｅｎｓｉｏｎ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＫ———Ｋｏｌｍｏｇｏｒｏｖｅｎｔｒｏｐｙ ,ｂｉｔｓ/ｓＲ…     

Comparative Study of Different Adaptive Window Protocols

We study an Adaptive Window Protocol (AWP) with general increase and decrease profiles in the presence of window dependent random losses. We derive a steady-state Kolmogorov equation, and then obtain its solution in analytic form for particular TCP versions proposed for high speed networks, such as Scalable TCP and HighSpeed TCP. We also relate window evolution under an AWP to workload process in queueing systems; this observation gives us a way to compare various AWP protocols.     

Learning to Predict Non-Deterministically Generated Strings

In this article we present an algorithm that learns to predict non-deterministically generated strings. The problem of learning to predict non-deterministically generated strings was raised by Dietterich and Michalski (1986). While their objective was to give heuristic techniques that could be used to rapidly and effectively learn to predict a somewhat limited class of strings, our objective is to give an algorithm which, though impractical, is capable of learning to predict a very general class. Our algorithm is meant to provide a general framework within which heuristic techniques can be effectively employed.     

Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

Entropy of fluidized bed—a measure of particles mixing

The paper presents an attempt to define the physical entropy of the dense phase of a fluidized bed, based on liquid-like properties of fluidized systems. The quantity U was used as the analogy of temperature in classical thermodynamics. The obtained expression for physical entropy was compared to the correlation suggested for the Kolmogorov entropy in bubbling bed.     

沸腾两相流动压力波动信号的特性

研究了竖直管内沸腾两相流动时压力波动信号的特性,对压力波动时间序列进行了标准偏差分析、功率谱密度函数分析、分维数分析、相关维分析及Kolmogorov熵分析,考察了热流密度对压力波动的影响规律.结果表明沸腾两相流动具有非线性混沌特性,且热流密度对各特征参数有不同影响.     

Measuring universal intelligence: Towards an anytime intelligence test

In this paper, we develop the idea of a universal anytime intelligence test. The meaning of the terms “universal” and “anytime” is manifold here: the test should be able to measure the intelligence of any biological or artificial system that exists at this time or in the future. It should also be able to evaluate both inept and brilliant systems (any intelligence level) as well as very slow to very fast systems (any time scale). Also, the test may be interrupted at any time, producing an approximation to the intelligence score, in such a way that the more time is left for the test, the better the assessment will be. In order to do this, our test proposal is based on previous works on the measurement of machine intelligence based on Kolmogorov complexity and universal distributions, which were developed in the late 1990s (C-tests and compression-enhanced Turing tests). It is also based on the more recent idea of measuring intelligence through dynamic/interactive tests held against a universal distribution of environments. We discuss some of these tests and highlight their limitations since we want to construct a test that is both general and practical. Consequently, we introduce many new ideas that develop early “compression tests” and the more recent definition of “universal intelligence” in order to design new “universal intelligence tests”, where a feasible implementation has been a design requirement. One of these tests is the “anytime intelligence test”, which adapts to the examinee's level of intelligence in order to obtain an intelligence score within a limited time.     

Kolmogorov complexity and combinatorial methods in communication complexity

We introduce a method based on Kolmogorov complexity to prove lower bounds on communication complexity. The intuition behind our technique is close to information theoretic methods.We use Kolmogorov complexity for three different things: first, to give a general lower bound in terms of Kolmogorov mutual information; second, to prove an alternative to Yao’s minmax principle based on Kolmogorov complexity; and finally, to identify hard inputs.We show that our method implies the rectangle and corruption bounds, known to be closely related to the subdistribution bound. We apply our method to the hidden matching problem, a relation introduced to prove an exponential gap between quantum and classical communication. We then show that our method generalizes the VC dimension and shatter coefficient lower bounds. Finally, we compare one-way communication and simultaneous communication in the case of distributional communication complexity and improve the previous known result.