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Digital tomosynthesis makes it possible to reconstruct multiple tomographs from digital data obtained during a single tomographic motion and permits digital processing, which adds a number of special advantages to the well-known advantages of conventional tomography. We performed digital tomosynthesis with a fluororadiographic TV unit with tomographic(More)
The paper proposes a novel approach to identification of continuous-time systems from sampled I/O data. The coefficients of plant transfer functions are directly identified by applying an iterative learning control which enables us to achieve perfect tracking for uncertain plants by iteration of trials. Furthermore, one way to make the method robust against(More)
The paper proposes a new iterative learning control for a class of linear continuous-time systems, which achieves high-precision tracking for uncertain plants by iteration of trials in the presence of heavy measurement noise. The robustness against measurement noise is achieved through (i) projection of continuous-time I/O signals onto a finite dimensional(More)
— The paper is concerned with both iterative learning control (ILC) and identification of continuous-time systems based on sampled I/O data in the presence of measurement noise. First, we propose a new ILC method which achieves tracking for uncertain plants by iteration of trials. The distinguished feature of this method is that (i) the leaning law works in(More)
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