Stock markets are dynamically changing in dynamics under uncertainty and risk. The notion of stock trading under uncertainty in stochastic investment trading systems that satisfies dynamic trading problems in market dynamics. This paper presents a novel approach for stock trading; we describe a framework which provides an effective basis upon which expert preferences, together with trader intuition, can be expressed about market dynamics under uncertainty. The proposed framework aims to aggregate collective expert preferences, including group sensibility and intuition, in order to provide a basis upon which stochastic investment trading systems can maximize investment returns and reduce high risk stocks in stock portfolio investments. Our approach uses Kansei evaluation and fuzzy reasoning with inference; this quantifies trader intuition relating to trading decisions based on dynamic market conditions under uncertainty. The framework is used in the quantification of Kansei, quantitative, and qualitative data sets; these are visualized using a Self-Organizing Map (SOM) to enable the selection of the optimal trading actions (buying, selling, and holding stocks) selected from stock portfolio investments. To confirm the model's performance the proposed approach has been tested in experiments using case studies based on stock trading; the results demonstrate that it performed well in real-world stock trading in various market conditions.

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We consider a simulation for radiation diffusion with consideration for geographic data. In our simulation scheme, each particle is simulated as a Markov chain with a transition function which is dependent on some geographic data, and we assume that the each particle moves independently. Then due to the effect of the law of large numbers, the real observed value can be approximated by the expectation of the Markov chain. In this paper, we simulate a large number of the paths of particles to see the influence of geographic data and analyze the structure called “Hot spot”.

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Fluctuations in heartbeat and interbreath intervals have been confirmed to exhibit scale-invariance. This property can be used to examine the effect of gender, aging, health or disease of a patient from his heartbeat and respiration signals. Sophisticated methods of scale-invariant signal analysis are based on the estimation of multifractal spectrum of the scale exponent. In this study we used an algorithm to estimate multifractal spectrum to confirm the existence of nonlinear relationship between heartbeat and interbreath interval under supine resting condition for 20 young and 20 elderly people. Using two border and one peak scale exponents which are selected from each spectrum, we constructed a 3-dimensonal feature space. The distance and the angle of the line connecting features estimated from heartbeat and respiratory dynamics for each patient were calculated and compared in both age group. Results showed that long range dependency in heartbeat dynamics degrades in elderly people which results to a large difference between heartbeat and respiratory dynamics. Exponents for heartbeat and respiratory dynamics for young people have no significant difference reinforcing the evidence of a possible nonlinear relationship of heartbeat and respiratory dynamics for young people.

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A brain-computer interface (BCI) is a translation system that detects human's intentions from EEG (electroencephalography) and converts it to computer commands. This system has been expected to help the disabled (e.g. amyotrophic lateral sclerosis or spinal cord injury patients) to communicate with the other people. In this paper, we tried to estimate shapes from EEG during tasks(gazing or imaging) shape in order to apply to the BCI system. The optimal combinations of parameters were investigated to discriminate shapes by using frequency analysis. Then features were used to recognize among three different shapes. As a result, accuracy of classification was more than 70 percent in some tasks. This fact suggests that a new type of BCI system will be available.

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From the inversion of the speech data of native and non-native speakers, we can find the difference in the vocal tract shape. Schroeder's theory assumes that the vocal tract is a uniform cylinder. The axial distribution of deformation of the cross sectional area is obtained as a perturbation from the uniform cylinder by applying the Boltzmann-Ehrenfest theorem. We extend Schroeder's theory to a case of the non-uniform cross sectional area of the vocal tract for an application to the foreign language learning process. Furthermore, we discussed constraint conditions to obtain a special solution to Schroeder's inverse problem and verifies it by numerical examples.

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The notion of spectral relative entropy rate is defined for jointly stationary Gaussian processes. Using classical information-theoretic results, we establish a remarkable connection between time and spectral domain relative entropy rates, and therefore with a multivariate version of the classical Itakura-Saito divergence. This information-theoretic result appears promising for applications where spectral entropy already plays an important role such as EEG analysis. It also lends support to a new spectral estimation technique recently developed by the authors where a multivariate version of the Itakura-Saito distance is employed as a spectrum divergence. A minimum complexity spectrum is provided by this new approach. Simulations suggest the effectiveness of the new technique in tackling multivariate spectral estimation tasks, especially in the case of short data records.

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For a linear scalar stochastic differential equation, we investigate its analytic solution by using Itô's formula and compute its numerical solution to show effects of noise. We also derive the confidence intervals for the solution of the linear scalar stochastic differential in this paper, which give more answers to why noise accelerates the convergence rate of the solution under stability with probability 1. The results in this paper suggest performance improvements of a control system in virtue of an appropriate fluctuation.

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In this paper, we consider automatic modulation classification (AMC) from received signals over flat fading channels. AMC is a technique to identify the modulation type of the transmitted symbols by using received symbols. First, we develop a method to determine the modulation type based on the Neyman-Pearson (NP) detector. Then, to reduce the numerical complexity, a method by using probability density function (PDF) approximation is derived. To certify our detector can be scarcely effected by the uncertainty in noise variance, we compare the case that the noise variance is available with the case that the noise variance is unavailable. Furthermore numerical simulations are conducted to assess the performance of our proposed methods even when only finite samples are limited.

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Car navigation systems display the car's position on a link, which is a line representing a road in map datum, when the car is running on a road and display its estimated position when it is running in a parking area. Therefore, it is necessary for map matching to decide whether a car is running on a road. The errors of the car's estimated position and the link position cause the incorrect decision when the car is running in a parking area parallel to the road. In this paper, a road departure decision method using statistic hypothesis tests and state diagrams is proposed. The statistic hypothesis tests make decisions considering the previous errors, and the state diagrams change decision conditions in each state. This method can therefore decrease the number of incorrect decisions that are made. The performance of this method was experimentally evaluated using observation data collected with a car, and the situation that this method could correctly decide on road departure was shown.

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We analytically solved the equations for Brillouin optical time domain reflectometry (BOTDR), which is a distributed strain/temperature sensing system that uses a property where the scattering spectrum shifts in proportion to the change of the strain/temperature. Although the equations include both spontaneous and stimulated Brillouin scattering terms, we show that the largest part of the measured spectrum arises from spontaneous scattering. We also show that the observed spectrum is analytically represented by ignoring the term related to stimulated scattering.

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The aerodynamic behavior of a tennis ball is completely different from other sports balls due to the size and the ball surface structure. The tennis ball goes through three distinct stages during the normal shot. These stages are racket/ball impact, trajectory through the air and then the ball/surface impact. The main objective of this research is to investigate experimentally and numerically the trajectories of a ball hit 38.9 m/s at various angles. Also the study is focused on the effect of the drag force and the turbulence around the ball. In the numerical analysis, the computational fluid dynamic CFD code, FLUENT for 2D and 3D configurations has been employed. The use of the numerical tool has resulted in a detailed investigation of the forces which act on the ball, aerodynamic forces to facilitate the ball and the ball rebound.

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