This paper examines the operational skills for the search activity via a remote-controlled robot to find out some hidden objects under debris, which simulates a robotic surrogate system for human rescuers. Our skill analysis revealed that the accurate and effective navigation of the robot requires the adequate view control of the camera. This operational strategy enables the parallel execution of the robotic ambulatory movement and the accurate environment recognition, ensuring the necessary cognitive resources available or externalized for human epistemic actions. Based upon the analyses, we discuss the necessary considerations to mitigate the cognitive restrictions imposed by the mechanical systems utilized and what encourages the naturalistic skill performance by the human operators, in terms of their ecological perspectives.
This paper considers multiobjective linear programming problems involving fuzzy random variable coefficients and examines interactive decision making to derive a satisficing solution for a decision maker. After introducing a fuzzy goal for each objective function, a problem to maximize a degree of possibility that each objective function satisfies the fuzzy goal is formulated. By considering the fact that the degree of possibility varies randomly, a fuzzy random programming model to minimize the variance of the degree, which is based on the variance minimization model in stochastic programming, is proposed. In order to find a satisficing solution for a decision maker, an interactive algorithm based on the reference point method is constructed. Finally, a numerical example of a production planning problem in agriculture is provided to demonstrate the usefulness of the proposed model.
By the former clustering algorithms, it is difficult to obtain a good clustering results for the data which is not able to be separated on linear classification boundary. The square of norms between data is used as dissimilarity so that those algorithms regard the classification boundary as linear. In this paper, the new hierarchical algorithm is proposed using kernel functions. The kernel functions are useful into the field of classification and give the value of the inner product of two vectors in a high-dimensional feature space by a mapping which one-to-one corresponds to the kernel function. The mapping is not explicit so that the value of the inner product of the feature space can be easily calculated on dimension of the original pattern space. By introducing the kernel functions, the proposed algorithm can separate the data on nonlinear classification boundary. Moreover, the availability of proposed algorithm is discussed through some numerical examples.
In this paper, we summarize the statistical mechanical representation of fuzzy clustering. Then, we give a framework of a possibilistic clustering based on a Bose-Einstein type membership function, and examine its clustering mechanisms. The fuzzy c-means clustering (FCM) method regularized with Shannon entropy gives the Maxwell-Boltzmann (or Gibbs) distribution function as a membership function. Similarly, by introducing fuzzy entropy to the FCM, we obtain the Fermi-Dirac type membership function. In these cases, the constraint that the sum of all particles is fixed is correspondent with the normalization constraint in fuzzy clustering. Furthermore, it is known that the state in which the total number of particles is not conserved exists and written by the Bose-Einstein distribution function. Thus, by the analogy of statistical mechanics, we obtain the Bose-Einstein type membership function without the constraint of normalization and propose a new fuzzy clustering algorithms.
Fuzzy integrals can be used as smoothing filters. Smoothness after filtering of an initially uniformly distributed image was investigated from two points of view i.e., a variance of the image and a correlation between two pixels in the equal distance. Formulations of the correlation for four integrals, Arithmetic averaging, Sugeno, median and opposite Sugeno integral, were studied. As for Choquet integral, an exact and simple formula was derived. As for other three integrals, approximated formulas as n→∞ were estimated, and they showed good coincidence even if n was a sufficiently small number 9.