ALBERT

Notizen: Effect of the growth-induced anisotropy field HKG has been investigated for the anomalous increase of the effective anisotropy field change Δ(Hk−4πMs) caused by annealing in heavily ion-implanted magnetic bubble garnets. In heavily implanted garnets, Δ(Hk−4πMs) increases with decreasing strain during annealing and does not follow the same curve that is obtained during implantation. The maximum difference in Δ(Hk−4πMs) between annealed and as-implanted samples corresponds to the value of Δ(Hk−4πMs) when the strain is reduced to zero by annealing. The anomalous increase is small in HKG-suppressed samples and is not observed in garnet films (YIG) without HKG. It is considered that the anomalous increase is due to suppression of the growth-induced anisotropy by heavy implantation. However, in the case of H+2 implantation the increase of Δ(Hk−4πMs) during the annealing process cannot be explained by the HKG suppression effect.

Notizen: The bunch length was measured by using a highly sensitive streak camera with a time resolution of 2 ps. It was found that fine structures appeared in the electron bunch shape and that the shapes of electron bunches were described by a Gaussian distribution on the average. The dependence of bunch length on beam current was measured for an electron beam of 607 MeV. The bunch length was well represented by a power function of beam current with an exponent of 0.197 at currents lower than 35 mA or 0.30 at high currents. The experimental results suggest that the longitudinal coupled-bunch beam instability takes place at low beam currents and the turbulent instability dominates at high currents. It was also found from the three-dimensional bunch shape measurements that the bunch shape tended to blow up at high currents.

Notizen: It is well known that the effective anisotropy field change Δ(Hk−4πMs) in as-He+/Ne+-implanted garnet films reaches a maximum value (2500 Oe) and then decreases with increasing the ion dose. It has been also reported that the Δ(Hk−4πMs) is recovered to the maximum value by annealing in the heavily implanted films. We have found an anomalous increase in Δ(Hk−4πMs) for the films which are heavily implanted with He+ or Ne+ and subsequently annealed at high temperature (350–600 °C). With annealing, Δ(Hk−4πMs) increases with annealing time and reaches a maximum value (5000 Oe for He+ implantation). It should be noticed that this maximum value is 2500 Oe larger than that in as-implanted films. This anomalous increase has been observed only for the films whose initial strain is more than about 1.5%. This effect has not been observed in heavily H+2 implanted films. This difference may be related to the type of damage induced by He+/Ne+ implantation and H+2 implantation. Using the above-mentioned effect, 4-μm-period ion-implanted propagation tracks have been fabricated and tested . The ion implantation condition was 50 keV/1×1014 Ne cm−2+200 keV/4.5×1014 Ne cm−2. Δ(Hk−4πMs) in the as-implanted film was 1900 Oe. After 560 °C annealing, it increased to 3700 Oe. For the 4-μm-period contiguous disk propagation tracks, 10% bias field margin has been obtained.

Notizen: Hybrid bubble memory devices using ion-implanted and Permalloy bubble propagation tracks have been proposed and developed. In this paper, the reduction of the cell size from 4 to 3 μm will be discussed. In the hybrid devices, the minor loops are composed of ion-implanted tracks and the functions are composed of Permalloy tracks. To reduce the cell size, therefore, both of the tracks should be improved. For the 3-μm-period ion-implanted tracks, the bubble diameter is reduced from 1 to 0.8 μm. According to the reduction of a bubble diameter, magnetostrictive anisotropy Δλ=λ100−λ111 is increased from 5 to 8×10−6 because the Sm content is increased to increase the anisotropy field Hk. The large Δλ affects the characteristics of inside and outside turns in the ion-implanted tracks. To get a good margin for the turns, the anisotropy field change induced by ion implantation ΔHk should be larger than in 4-μm-period tracks. There is a new problem in 3-μm-period hybrid devices. In the hybrid devices, there is no deep ion implantation in the Permalloy track region, while there is a deep ion implantation in the ion-implanted track region. The effective bubble height is, therefore, different in the ion-implanted and Permalloy track region, and the bias field region where bubbles exist stable is different. In the typical 3-μm-period devices, the bias field region of the ion-implanted tracks is 20–30 Oe lower than that of the Permalloy tracks. The operating bias field adjustment (OBA) is needed. We have checked some OBA methods and found that the thinning of the garnet film in the Permalloy track region is a good method. Thinning the garnet film partially, the operating bias field can be adjusted easily and there is no harmful effect.

Notizen: Abstract In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n+1)-th iteration is a sum of the motor command in then-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in then-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes.

Notizen: Abstract In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum — magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum — parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural netowrk model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available. Consequently, the model not only accounts for the learning and control capability of the CNS, but also provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known.

Notizen: Abstract In this paper, we study trajectory planning and control in voluntary, human arm movements. When a hand is moved to a target, the central nervous system must select one specific trajectory among an infinite number of possible trajectories that lead to the target position. First, we discuss what criterion is adopted for trajectory determination. Several researchers measured the hand trajectories of skilled movements and found common invariant features. For example, when moving the hand between a pair of targets, subjects tended to generate roughly straight hand paths with bell-shaped speed profiles. On the basis of these observations and dynamic optimization theory, we propose a mathematical model which accounts for formation of hand trajectories. This model is formulated by defining an objective function, a measure of performance for any possible movement: square of the rate of change of torque integrated over the entire movement. That is, the objective function C T is defined as follows: $$C_T = \frac{1}{2}{}^t\int\limits_0^f {\sum\limits_{i = 1}^n {\left( {\frac{{{\text{d}}z_i }}{{{\text{d}}t}}} \right)^2 {\text{d}}t,} } $$ where z iis the torque generated by the i-th actuator (muslce) out of n actuators, and t fis the movement time. Since this objective function critically depends on the complex nonlinear dynamics of the musculoskeletal system, it is very difficult to determine the unique trajectory which yields the best performance. We overcome this difficult by developing an iterative scheme, with which the optimal trajectory and the associated motor command are simultaneously computed. To evaluate our model, human hand trajectories were experimentally measured under various behavioral situations. These results supported the idea that the human hand trajectory is planned and controlled in accordance with the minimum torquechange criterion.

Notizen: Abstract The superconducting compound Nb3Sn was produced by the reduction of the oxides or hydroxides of niobium and tin. The procedure consists of the following three steps; (i) preparation of the mixed oxides or hydroxides, (ii) direct alloy reduction, and (iii) homogenizing heat treatment of the reduced metal powders. For mixing the two oxides, two methods were tried: the simultaneous precipitation from the aqueous solution and the solidification of the molten oxides. These mixed oxides were reduced by ca!ciunn or magnesium vapour in the temperature range 973 to 1373 K. After calcium or magnesium oxide was removed by leaching the fine compound powder had an A-15 type crystal structure, but showed a relatively low superconducting critical temperature (T c). The isothermal annealing improvedT c to 18.0 K.