Development of a Model based on Evaluation Considering Explicit and Implicit Element in Multiple Criteria Decision Making

The Analytic Hierarchy Process (AHP) is a decision-making method for smoothly managing problems, criteria, and alternatives. AHP can be used to respond to multiple criteria, and allows for the quantification of subjective human judgments, as well as objective evaluations. In a classical AHP, a decision-maker derives a list of priorities by consciously comparing criteria and alternatives in order to deriving a comprehensive evaluation. However, when the number of criteria increases, the problem also becomes complicated and the subjective judgment of the decision-maker tends to be clouded by ambiguity and inconsistency. As the solution, this study proposes a method whereby latent elements are extracted from the data given by the decision-maker, and an evaluation is made from a different aspect based on the extracted elements. This allows for the construction of a model in which a decision is made from both explicit and implicit elements by making a final synthesis of the results obtaine d using the conventional method as well as the evaluation obtained using the method proposed in this study. As a result, we can conclude that it is possible to make a decision that is not affected by the ambiguity or inconsistency of the decision-maker

Fast and Robust Reconstruction of Penumbral Images by Combining Multiple Wiener Filters

Penumbral imaging is a powerful technique for imaging with penetrating radiation such as neutrons and hard x rays. The reconstructed image can be obtained by the deconvolution of the detected image. Usually a Wiener filter is used for the reconstruction. The limitation of the Wiener filter is that it will introduce a significant distortion when the detected image contains noise. On the other hand, heuristic methods can obtain clear reconstructed image from noisy penumbral images. However, it significantly takes computation time. We proposed a new reconstruction method in the penumbral imaging to obtain clear reconstructed image by combining the multiple Wiener filters and the method can reduce computation time. The effectiveness of the proposed method has been demonstrated by computer simulation and real laser plasma experiment

Fast and Robust Reconstruction of Penumbral Images by Combining Multiple Wiener Filters * )

Penumbral imaging is a powerful technique for imaging with penetrating radiation such as neutrons and hard x rays. The reconstructed image can be obtained by the deconvolution of the detected image. Usually a Wiener filter is used for the reconstruction. The limitation of the Wiener filter is that it will introduce a significant distortion when the detected image contains noise. On the other hand, heuristic methods can obtain clear reconstructed image from noisy penumbral images. However, it significantly takes computation time. We proposed a new reconstruction method in the penumbral imaging to obtain clear reconstructed image by combining the multiple Wiener filters and the method can reduce computation time. The effectiveness of the proposed method has been demonstrated by computer simulation and real laser plasma experiment

A New De-Noising Method of Laser-Produced Plasma Penumbral Images by Principal Component Analysis

Penumbral imaging technique can be applied to highly penetrating radiations such that of as neutrons. In penumbral imaging, the source image can be recovered from its penumbral image by deconvolution. The method is an efficient imaging technique for fast ignition research. However, the γ rays produced by the fast-heating laser pollute the penumbral image as noise. Conventional deconvolution methods like the Wiener filter cannot obtain a clear reconstructed image from noisy penumbral image. In this paper, we proposea new reconstruction method by principal component analysis (PCA). The method can efficiently remove the noise by “training” images obtained from other experiments. We used the (2D)^2PCA method as a noise reduction method, which is one of the PCA methods. The efficacy of the proposed method is demonstrated by computer simulation