Automatic Transaction of Signal via Statistical Modeling
The statistical information processing can be characterized by using the likelihood function defined by giving an explicit form for an approximation to the true distribution from which the data are generated. This mathematical representation as an approximation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike ( and ) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler information which defines the similarity between the predictive distribution of the model and the true distribution and proposed the Akaike information criterion (AIC). By the use of this AIC, it becomes possible to evaluate and compare the goodness of many models objectively and it enables us to select the best model among many candidates. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this study, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. In each case, the AIC plays an important role in making the procedure automatic and objective, and promises to realize a detail examination of a large amount of data sets, which provides us with an opportunity to discover new information.

title: Detection of the Structure of Particle Velocity Distribution by Finite Mixture Distribution Model
abstract: From direct satellite observations of space plasma, we have obtained many significant results by means of velocity moments of particle velocity distribution functions (e.g. number density, bulk velocity and temperature). This fluid description assumes that the plasma is able to maintain local thermal equilibria. When plasma is in a state of thermal equilibrium, a distribution function of particle velocity is given as a normal distribution which is called Maxwellian in a field of the plasma physics. In the meantime, observational techniques has progressed notably and has made it possible to measure more detailed shape of distribution function in the three-dimensional velocity space. Those observations revealed that there are many cases where space plasmas have not reached a state of thermal equilibrium and their velocity distributions are not a single Maxwellian but consist of multiple peaks. This distribution may occur since space plasma such as in the solar wind is basically collisionless with large mean-free-path (about 1 AU). It may give the same velocity moments even if the shapes of distribution functions are different. For instance, when the plasma with two beam components whose velocity vectors are sunward and anti-sunward, respectively, and the numbers of particles composing each component are the same, bulk velocity would become zero from calculation. On the other hand, when stagnant plasma are observed, bulk velocity would also become zero. In the two-beam case, it would be necessary to separate into two beams and calculate the velocity moments for each beam. It has been difficult, however, to evaluate the shape of distribution function especially when more than one components partially overlap each other. It produces a serious problem especially when we treat many multi-component cases statistically. In this paper, we construct a method of representing three-dimensional distribution function by finite mixture distribution model in which the parameter values are obtained by EM (Expectation-Maximization) algorithm[1]. With this method, we can express the shape of the function and find a possible way to conduct a statistical analysis.

title: Application of Multivariate Maxwellian Mixture Model to Plasma Velocity Distribution
abstract: Recent space plasma observations provide us three-dimensional velocity distributions which are found to have multiple peaks. We propose a method for analyzing such a velocity distribution via a multivariate Maxwellian mixture model whose each component represents each of the multiple peaks. The parameters of the model are determined through the EM algorithm. For an auto judgment of preferable number of components of the mixture model, we introduce a method of examining the number of extremum of the resulting mixture model. We show applications of our method to observations in the plasma sheet boundary layer and in the central plasma sheet in the terrestrial magnetosphere.

title: Design and Control of a Fish-like Robot Using an Electrostatic Motor
abstract: This paper presents a project that aims at constructing a biologically inspired fish-like robot. The robot is designed to be capable of propelling itself through oscillations of a flexible caudal fin, like a real underwater fish. In particular, the caudal fin is driven by a mechanism actuated by a unique actuator called electrostatic film motor. In this paper, the dynamics of the electrostatic film motor are briefly introduced so as to well understand its characteristics and behavior. Based on the theoretical analysis and several design considerations inspired by biological concepts, we realize the fish-like robot actuated by an electrostatic film motor and propose swimming control methods for it. Experiments are carried out to confirm the validity of the original design and control. The current robot achieves fish-like maneuvering and approximate velocity of 0.018 m/s in dielectric liquid.