Compatibility relationship among reaction equilibria, equivalence principle of reaction approaches, and silicon contamination in semiconductors

This paper discovers some shortcomings in the algorithm for the incorporation of Si into GaAs in the GaAs VPE process. These faults arise from neglecting a link, the compatibility relationship, in chemical thermodynamics. The meaning of said relationship is as follows: In an equilibrium complex system, each species can only contribute one and the same quantity (its equilibrium quantity) to the different equilibria of the various reactions involving it; yet even under this restriction, every equilibrium constant is satisfied, and all the reaction equilibria coexist compatibly in the system. Only by adding the relationship can the equilibrium theory for the complex system be complete. This paper also tells its position in chemical thermodynamics. Such a compatibility concept directly leads to an equivalence principle: In a complex system, a certain species can usually be simultaneously formed by many chemical reactions; when the system has reached equilibrium under fixed environmental conditions, the equilibrium quantity of said species calculated according to each chemical equation of these reactions will be equal and the various reaction approaches will be equivalent, provided that for all the reactants and all the other products of these reactions their equilibrium quantities in the system are respectively taken as corresponding knowns for the calculations, which is extremely useful for seeking a functional relation among the species' equilibrium quantities in a system (Si contamination is one of the examples). Under the guidance of those arguments, the various schools' algorithms for the Si contamination can be uniformized and simplified, and the contamination quantity relation between Si and O, two very important impurities, is found.

algorithms for computing weak bisimulation equivalence

IEEE Comp Soc, IFIP, Tianjin Normal Univ

A study of the crystallographic equivalence of ether and ketone groups in poly(aryl ether ketone)s

Crystallographic equivalence of ether and ketone in all para-substituted PAEKs crystallized in form I was discussed in this paper. In a word, crystallographic equivalence between ether and ketone groups is tenable when polymer contains only phenyl rings in the repeat unit. If a polymer contains a diphenyl group in the repeat unit, two cases should be distinguished. In the case of PEDEKK and PEEKDK, crystallographic equivalence between ether and ketone linkages is untenable, However, in the case of PEDK and PEDEKDK, crystallographic equivalence between ether and ketone linkages is still tenable.

Interparticle distance-temperature-strain rate equivalence for the brittle-tough transition in polymer blends

From the angle of energy transformation an equation was obtained for the brittle transition in polymer blends. The effects of interparticle distance, temperature and strain rate on the brittle-tough transition in polymer blends were characterized by this equation. The calculations show that, for this transition: (1) increasing temperature and decreasing interparticle distance are equivalent and the shift factor increases with increasing temperature; (2) decreasing strain rate and decreasing interparticle distance have equivalent effects on the transition; (3) the strain rate must be optimum in order to find the brittle-tough transition phenomena for a given temperature region. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

Roles of Knowledge in Motor Learning

The goal of this thesis is to apply the computational approach to motor learning, i.e., describe the constraints that enable performance improvement with experience and also the constraints that must be satisfied by a motor learning system, describe what is being computed in order to achieve learning, and why it is being computed. The particular tasks used to assess motor learning are loaded and unloaded free arm movement, and the thesis includes work on rigid body load estimation, arm model estimation, optimal filtering for model parameter estimation, and trajectory learning from practice. Learning algorithms have been developed and implemented in the context of robot arm control. The thesis demonstrates some of the roles of knowledge in learning. Powerful generalizations can be made on the basis of knowledge of system structure, as is demonstrated in the load and arm model estimation algorithms. Improving the performance of parameter estimation algorithms used in learning involves knowledge of the measurement noise characteristics, as is shown in the derivation of optimal filters. Using trajectory errors to correct commands requires knowledge of how command errors are transformed into performance errors, i.e., an accurate model of the dynamics of the controlled system, as is demonstrated in the trajectory learning work. The performance demonstrated by the algorithms developed in this thesis should be compared with algorithms that use less knowledge, such as table based schemes to learn arm dynamics, previous single trajectory learning algorithms, and much of traditional adaptive control.

Motor Control and Learning by the State Space Model

A model is presented that deals with problems of motor control, motor learning, and sensorimotor integration. The equations of motion for a limb are parameterized and used in conjunction with a quantized, multi-dimensional memory organized by state variables. Descriptions of desired trajectories are translated into motor commands which will replicate the specified motions. The initial specification of a movement is free of information regarding the mechanics of the effector system. Learning occurs without the use of error correction when practice data are collected and analyzed.

Growth of Motor Coordination in Early Robot Learning

M. H. Lee and Q. Meng, Growth of Motor Coordination in Early Robot Learning, IJCAI-05, 2005.

Speeding up the learning of equivalence classes of Bayesian network structures

R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.

Using ant colony optimisation in learning Bayesian network equivalence classes

R. Daly, Q. Shen and S. Aitken. Using ant colony optimisation in learning Bayesian network equivalence classes. Proceedings of the 2006 UK Workshop on Computational Intelligence, pages 111-118.

A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures

R. Daly and Q. Shen. A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 67-74.