pattern matching compilation of functions defined in context-free languages

LFC is a functional language based on recursive functions defined in context-free languages. In this paper, a new pattern matching algorithm for LFC is presented, which can represent a sequence of patterns as an integer by an encoding method. It is a rather simple method and produces efficient case-expressions for pattern matching definitions of LFC. The algorithm can also be used for other functional languages, but for nested patterns it may become complicated and further studies are needed.

encoding-based method for pattern matching

A new algorithm for compiling pattern matching is presented. Different from the traditional traversal-based approaches, it can represent a sequence of patterns as an integer by an encoding method and translate equations into case-expressions. The algorithm is simple to implement, and efficient for a kind of patterns, i.e. simple and dense patterns. This method can be complementary to traditional approaches.

Effect of channel surface wettability and temperature gradients on the boiling flow pattern in a single microchannel

The objective of this paper is to investigate the effects of channel surface wettability and temperature gradients on the boiling flow pattern in a single microchannel. The test section consists of a bottom silicon substrate bonded with a top glass cover. Three consecutive parts of an inlet fluid plenum, a central microchannel and an outlet fluid plenum were etched in the silicon substrate. The central microchannel had a width of 800 mu m and a depth of 30 mu m. Acetone liquid was used as the working fluid. High outlet vapor qualities were dealt with here. The flow pattern consists of a fluid triangle (shrinkage of the liquid films) and a connected long liquid rivulet, which is generated in the central microchannel in the timescale of milliseconds. The peculiar flow pattern is formed due to the following reasons: (1) the liquid rivulet tends to have a large contact area with the top hydrophilic channel surface of the glass cover, but a smaller contact area with the bottom silicon hydrophobic surface. (2) The temperature gradient in the chip width direction at the top channel surface of the glass cover not only causes the shrinkage of the liquid films in the central microchannel upstream, but also attracts the liquid rivulet populated near the microchannel centerline. (3) The zigzag pattern is formed due to the competition between the evaporation momentum forces at the vapor-liquid interfaces and the force due to the Marangoni effect. The former causes the rivulet to deviate from the channel centerline and the latter draws the rivulet toward the channel centerline. (4) The temperature gradient along the flow direction in the central microchannel downstream causes the breakup of the rivulet to form isolated droplets there. (5) Liquid stripes inside the upstream fluid triangle were caused by the small capillary number of the liquid film, at which the large surface tension force relative to the viscous force tends to populate the liquid film locally on the top glass cover surface.

A method of Biomimetic Pattern Recognition for face recognition

A new method of face recognition, based on Biomimetic Pattern Recognition and Multi-Weights Neuron Network, had been proposed. A model for face recognition that is based on Biomimetic Pattern Recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and K-Nearest Neighbor Rules showed that the method based on BPR can eliminate the error recognition of the samples of the types that not be trained, the correct rate is also enhanced.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

An improved BP algorithm for pattern recognition

An improved BP algorithm for pattern recognition is proposed in this paper. By a function substitution for error measure, it resolves the inconsistency of BP algorithm for pattern recognition problems, i.e. the quadratic error is not sensitive to whether the training pattern is recognized correctly or not. Trained by this new method, the computer simulation result shows that the convergence speed is increased to treble and performance of the network is better than conventional BP algorithm with momentum and adaptive step size.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.