Conventional researches on Wireless Power Transmission systems use experimental or numerical method to analyze the systems. However, the situation will be better if symbolic method is available. This paper develops a software which generates a state equation directly from schematics. The software helps engineers that design circuits and obtain characteristics of circuits to analyze the behavior...

For anthropologists and archaeologists, the study of cultural change is greatest challenge. Initially, subject was considered from perspectives that included too few variables, resulting in an approximation proved to be incomplete inadequate. Since end last century, important efforts have been made document a broader context, revealing variability processes involved. These new studies highlight...

Many high level representations of time series have been proposed for data mining. One representation that the data mining community has not considered in detail is the discretization of the original data into symbolic strings. At first glance this seems a surprising oversight. There is an enormous wealth of existing algorithms and data structures that allow the efficient manipulations of strin...

Currently statistical and artificial neural network methods dominate in financial data mining. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design and other applications. Traditionally symbolic methods prevail in the areas with significant nonnumeric (symbolic) knowledge, such as relative location in robot navigation. At first glance, st...

This brief paper presents a symbolic dynamicsbased method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during...

The choice of an appropriate representation remains crucial for mining time series, particularly to reach a good trade-o between the dimensionality reduction and the stored information. Symbolic representations constitute a simple way of reducing the dimensionality by turning time series into sequences of symbols. SAXO is a data-driven symbolic representation of time series which encodes typica...

A hybrid dynamical system is a mathematical model suitable for describing an extensive spectrum of multi-modal, time-series behaviors, ranging from bouncing balls to air traffic controllers. This paper describes multi-modal symbolic regression (MMSR): a learning algorithm to construct non-linear symbolic representations of discrete dynamical systems with continuous mappings from unlabeled, time...