Multiple model bank selection based on nonlinearity measure and H-gap metric

This paper provides a systematic method for model bank selection in multi-linear model analysis for nonlinear systems by presenting a new algorithm which incorporates a nonlinearity measure and a modified gap based metric. This algorithm is developed for off-line use, but can be implemented for on-line usage. Initially, the nonlinearity measure analysis based on the higher order statistic (HOS) and the linear cross correlation methods are used for decomposing the total operating space into several regions with linear models. The resulting linear models are then used to construct the primary model bank. In order to avoid unnecessary linear local models in the primary model bank, a gap based metric is introduced and applied in order to merge similar linear local models. In order to illustrate the usefulness of the proposed algorithm, two simulation examples are presented: a pH neutralization plant and a continuous stirred tank reactor (CSTR).     

Energy dependence of the effective mass in liquid3He

The striking behavior of the specific heat of liquid³He as function of temperature at low temperatures requires the effective massm* to change rapidly with temperature; this can be translated into a rapid variation with energy,m*/m dropping from itsE=0 value to 1 over the temperature range 0.5 K. We explore this effect in a model in which the enhancement of the effective mass is due to coupling to spin fluctuations. At very lowT50 mK, the variation in specific heat results fromT ³ lnT terms. The free energy, on the other hand, does not containT ² lnT terms in its dependence on the magnetic field, implying that the susceptibility, which is essentiallym*/(1+F ₀ ^a ), also does not have such logarithmic terms. Consequently, ifm* varies with energy, so mustF ₀ ^a , so as to leave the susceptibility free of this rapid variation. The rough constancy ofm*/(1+F ₀ ^a ) seems empirically to hold to higher energies and temperatures. Ifm*/m drops, with increasing energy, to unity, the spin-fluctuation theory, which is described in terms of Landau parameters at the Fermi surface, goes over into the paramagnon theory. The rapid change with energy of the effective interactions can be understood within the framework of response theory as a shaking off of the relevant collective modes with increasing frequency of the imposed oscillations. The changes in effective interactions have consequences for the interpretation of experiments involving inelastic neutron scattering from liquid³He.Also State University of New York, Stony Brook, New York. Supported in part by U.S. D.O.E. Contract DE-AC02-76ER13001.Also University of Illinois at Urbana—Champaign. Supported in part by U.S. NSF Grant DMR 81-21273.     

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

SATCHMORE was introduced as a mechanism to integrate relevancy testing with the model-generation theorem prover SATCHMO. This made it possible to avoid invoking some clauses that appear in no refutation, which was a major drawback of the SATCHMO approach. SATCHMORE relevancy, however, is driven by the entire set of negative clauses and no distinction is accorded to the query negation. Under unfavorable circumstances, such as in the presence of large amounts of negative data, this can reduce the efficiency of SATCHMORE. In this paper we introduce a further refinement of SATCHMO called SATCHMOREBID: SATCHMORE with BIDirectional relevancy. SATCHMOREBID uses only the negation of the query for relevancy determination at the start. Other negative clauses are introduced on demand and only if a refutation is not possible using the current set of negative clauses. The search for the relevant negative clauses is performed in a forward chaining mode as opposed to relevancy propagation in SATCHMORE which is based on backward chaining. SATCHMOREBID is shown to be refutationally sound and complete. Experiments on a prototype SATCHMOREBID implementation point to its potential to enhance the efficiency of the query answering process in disjunctive databases. Donald Loveland, Ph.D.: He is Emeritus Professor of Computer Science at Duke University. He received his Ph.D. in mathematics from New York University and taught at NYU and CMU prior to joining Duke in 1973. His research in automated deduction includes defining the model elimination proof procedure and the notion of linear resolution. He is author of one book and editor/co-editor of two other books on automated theorem proving. He has done research in the areas of algorithms, complexity, expert systems and logic programming. He is an AAAI Fellow, ACM Fellow and winner of the Herbrand Award in Automated Reasoning. Adnan H. Yahya, Ph.D.: He is an associate professor at the Department of Electrical Engineering, Birzeit University, Palestine. He received his Diploma and PhD degrees from St. Petersburg Electrotechnical University and Nothwestern University in 1979 and 1984 respectively. His research interests are in Artificial Intelligence in general and in the areas of Deductive Databases, Logic Programming and Nonmonotonic Reasoning in particular. He had several visiting appointments at universities and research labs in the US, Germany, France and the UK. Adnan Yahya is a member of the ACM, IEEE and IEEE Computer Society.     

Monitoring of multivariable dynamic processes and sensor auditing

Industrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross correlated, and collinear. A statistical process monitoring (SPM) method based on state variables is introduced to monitor such processes. The statistical model that describes the in-control variability is based on a canonical variate (CV) state space model. The CV state variables are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. A T² statistic based on the CV state variables is utilized for developing the SPM procedure. The CV state variables are also used for monitoring sensor reliability. An experimental application to a high temperature short time (HTST) pasteurization process illustrates the proposed methodology.     

Some Generalised Characteristics of the Electro-dynamics of the Plasma Focus in Its Axial Phase: Illustrated by an Application to Independantly Determine the Drive Current Fraction and the Mass Swept-Up Fraction

We describe the axial phase of the Mather plasma focus by two coupled equations of motion and circuit. We non-dimensionalised these equations resulting in two coupled equations which are characterised by only three scaling parameters α, β and δ which are ratios of electrical to transit times, inductances and impedances respectively. The normalised current waveform, trajectory and speed profile are unique for each combination of α, β, δ which are the ratios of characteristic times (electrical discharge vs. axial transit), inductances (tube inductance vs. static inductance) and impedances (stray resistance vs. electrical surge impedance). This leads to important information and insight into various aspects of the axial phase. In the present work we show that in a time-matched plasma focus shot we deduce the value of axial phase current fraction f_c simply by measuring the calibrated voltage waveform and the uncalibrated current waveform. The scaling parameters β and δ are fixed; and by form-fitting the measured current waveform to the normalised current waveform using the value of α of the shot is determined uniquely; from which the peak current and the ratio of peak to average speed [the speed form factor (SFF)] are obtained. The average transit speed is measured by time-of-flight using the voltage upturn as indicator of end of axial phase. Then the SFF yields the peak speed. The measured voltage (back EMF), peak current and peak axial speed (all at the end of axial phase) allows the unambiguous measurement of f_c. The value of the mass swept-up fraction f_m is deduced from α which is the ratio of the characteristic discharge and the characteristic transit times, both deduced during the non-dimensionalisation of the equations. Analysis of a time-matched shot in the INTI PF at 15 kV, 3 Torr D₂ gave f_c = 0.68 and f_m = 0.05.     

正电子湮没研究Al、Nb共掺CaCu3Ti4O12陶瓷高介电常数机理

关于CaCu₃Ti₄O₁₂陶瓷的高介电常数机理,目前广泛接受的是非本征的内阻挡层电容模型。该模型认为在多晶中元素变价、缺陷和非化学计量比等导致的半导化晶粒被绝缘晶界层,即内阻挡层所包围。其中内阻挡层的厚度对材料的介电性能影响较大,而扫描电子显微镜（SEM）测试表明样品晶界呈稀烂的果酱状,SEM难以测量晶界区域绝缘内阻挡层厚度。本文采用正电子湮没技术表征其厚度,通过对CaCu₃Ti₄O₁₂陶瓷共掺不同浓度的Al、Nb（CaCu₃Ti_4-xAl_0.5xNb_0.5xO₁₂,x=0.2%、0.5%、5.0%）改变其晶粒和晶界的微观结构,研究CaCu₃Ti₄O₁₂陶瓷高介电常数机理。正电子湮没结果显示,掺杂样品符合多普勒展宽谱S参数的变化趋势与平均寿命的变化趋势一致。x=0.5%掺杂样品的介电常数最高,其平均寿命、S参数和湮没长寿命成分均最小,阻挡层最薄。实验结果验证了描述CaCu₃Ti₄O₁₂陶瓷高介电常数机理的内阻挡层电容模型的预测。     

基于穆斯堡尔参数的人工神经网络识别矿物的方法

采用改进型反向传播人工神经网络,以含铁矿物穆斯堡尔参数作为样本,通过神经网络的训练,能很好地识别的,从而有效地提高了对矿物的识别本领。     

Reactions with 2,3,4,5-Tetrahydro-benzo[b]thiepin-5-one and its Derivatives

2,3,4,5-Tetrahydro-benzo[b]thiepin-5-one, its 7-methyl derivative, and their S, S-dioxides condense with aromatic aldehydes to give the corresponding 4-arylidene derivatives 3 and 4 . Compounds 4 react with hydrazine and phenylhydrazine to yield 3, 3a, 4, 5-tetrahydro-2 H-benzo-[b]thiepino[4, 5-c]pyrazole S, S-dioxide derivatives 5 and 6 . Some of the arylidene derivatives 4 add GRIGNARD reagents to give 7 . The IR spectra of representative examples are discussed.