Syllables as processing units in handwriting production.

This research focused on the syllable as a processing unit in handwriting. Participants wrote, in uppercase letters, words that had been visually presented. The interletter intervals provide information on the timing of motor production. In Experiment 1, French participants wrote words that shared the initial letters but had different syllable boundaries. In Experiment 2, French- and Spanish-speaking participants wrote cognates and pseudowords with a letter sequence that was always intrasyllabic in French and intersyllabic in Spanish. In Experiment 3, French-Spanish bilinguals wrote the cognates and pseudowords with the same type of sequences. In the 3 experiments, the critical interletter intervals were longer between syllables than within syllables, indicating that word syllable structure constrains motor production both in French and Spanish. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Substrate-mediated interactions and intermolecular forces between molecules adsorbed on surfaces

Adsorbate interactions and reactions on metal surfaces have been investigated using scanning tunneling microscopy. The manners in which adsorbates perturb the surface electronic structure in their vicinity are discussed. The effects these perturbations have on other molecules are shown to be important in overlayer growth. Interactions of molecules with surface steps are addressed, and each molecule's electron affinity is shown to dictate its adsorption sites at step edges. Standing waves emanating from steps are demonstrated to effect transient molecular adsorption up to 40 A away from the step edge. Halobenzene derivatives are used to demonstrate how the surface is important in aligning reactive intermediates.     

Investigations of ZnO thin films deposited by a reactive pulsed laser ablation

Highly transparent ZnO thin films were deposited at different substrate temperatures by pulsed laser deposition in an oxygen atmosphere. The thin films were characterized by various techniques including X-ray diffraction, scanning electron microscopy, optical absorption, and photoluminescence. We demonstrated that oriented wurtzite ZnO thin films could be deposited at room temperature using a high purity zinc target. Variable temperature photoluminescence revealed new characteristics in the band edge emission. The underlying mechanism for the observed phenomena was also discussed.     

Granular neural networks for numerical-linguistic data fusion andknowledge discovery

We present a neural-networks-based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include 1) how to make neural networks process both numerical and linguistic data in a database, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to use neural networks to do numerical-linguistic data fusion, 4) how to use neural networks to discover granular knowledge from numerical-linguistic databases, and 5) how to use discovered granular knowledge to predict missing data. In order to answer the above concerns, a granular neural network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database. The GNN is also capable of greatly compressing low-level granular data to high-level granular knowledge with some compression error and a data compression rate. To do KDDM in huge databases, parallel GNN and distributed GNN will be investigated in the future.     

Parameterized fuzzy operators in fuzzy decision making

The basic operations of fuzzy sets, such as negation, intersection, and union, usually are computed by applying the one‐complement, minimum, and maximum operators to the membership functions of fuzzy sets. However, different decision agents may have different perceptions for these fuzzy operations. In this article, the concept of parameterized fuzzy operators will be introduced. A parameter α will be used to represent the degree of softness. The variance of α captures the differences of decision agents' subjective attitudes and characteristics, which result in their differing perceptions. The defined parameterized fuzzy operators also should satisfy the axiomatic requirements for the traditional fuzzy operators. A learning algorithm will be proposed to obtain the parameter α given a set of training data for each agent. In this article, the proposed parameterized fuzzy operators will be used in individual decision‐making problems. An example is given to show the concept and application of the parameterized fuzzy operators. © 2003 Wiley Periodicals, Inc.     

Complex fuzzy logic

A novel framework for logical reasoning, termed complex fuzzy logic, is presented in this paper. Complex fuzzy logic is a generalization of traditional fuzzy logic, based on complex fuzzy sets. In complex fuzzy logic, inference rules are constructed and "fired" in a manner that closely parallels traditional fuzzy logic. The novelty of complex fuzzy logic is that the sets used in the reasoning process are complex fuzzy sets, characterized by complex-valued membership functions. The range of these membership functions is extended from the traditional fuzzy range of [0,1] to the unit circle in the complex plane, thus providing a method for describing membership in a set in terms of a complex number. Several mathematical properties of complex fuzzy sets, which serve as a basis for the derivation of complex fuzzy logic, are reviewed in this paper. These properties include basic set theoretic operations on complex fuzzy sets - namely complex fuzzy union and intersection, complex fuzzy relations and their composition, and a novel form of set aggregation - vector aggregation. Complex fuzzy logic is designed to maintain the advantages of traditional fuzzy logic, while benefiting from the properties of complex numbers and complex fuzzy sets. The introduction of complex-valued grades of membership to the realm of fuzzy logic generates a framework with unique mathematical properties, and considerable potential for further research and application.     

Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.     

On the Minimum Common Supergraph of Two Graphs

The minimum common supergraph of two graphs, g₁ and g₂, is defined as the smallest graph that includes, as subgraphs, both g₁ and g₂. It is shown that minimum common supergraph computation can be solved by means of maximum common subgraph computation. For the latter problem, algorithms are known from the literature. It will also be shown that for a certain class of cost functions, the concept of graph edit distance is closely related with the minimum common supergraph.     

On the Properties of Fuzzy Switching Functions

Abstract

A strictly binary approach to the treatment of switching circuits today is not always adequate to describe systems in the real world. This approach is partly due, to the relative simplicity of designing binary switching systems, and to the fact that basic switching modules in common use are two-positional. Consequently, every variable in Boolean logic is assumed to be two-valued. However, because of real-world constraints, the attributes of system variables are often ambiguously defined. In other words, quite often variables might have values other than falsehood and truth. Cases with such attributes arise, for example in artifical intelligence and related subjects. Ever since Zadeh introduced the idea of fuzzy set theory [11 by utilizing the concept of membership grade, a number of authors have been concerned with the analysis and applications of fuzzy models. Especially, the relation of fuzzy to switching systems have been discussed in [2]–[12] and by other researchers in relation to other topics.

In this paper we are concerned with the study of fuzzy switching functions and their properties. Special attention is devoted to their minimization, enumeration, and their application to Boolean static hazard detection.