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This paper presents the design and control of a novel assistive robotic walker that we call "JAIST active robotic walker (JARoW)". JARoW is developed to provide potential users with sufficient ambulatory capability in an efficient, cost-effective way. Specifically, our focus is placed on how to allow easier maneuverability by creating a natural interface between the user and JARoW. For the purpose, we develop a rotating infrared sensor to detect the user's lower limb movement. The implementation details of the JARoW control algorithms based on the sensor measurements are explained, and the effectiveness of the proposed algorithms is verified through experiments. Our results confirmed that JARoW can autonomously adjust its motion direction and velocity according to the user's walking behavior without requiring any additional user effort.

We present absolutely calibrated far-ultraviolet spectrophotometry of the quasar 3C 273 covering the 900-1800 Å range. Our ∼ 3 Å resolution spectra were obtained with the Hopkins Ultraviolet Telescope during the Astro-1 mission in December 1990 and during the Astro-2 mission in March 1995. Both spectra exhibit a change in slope near the Lyman limit in the quasar rest frame. At longer UV wavelengths, the continuum has a power-law index of 0.5-0.7, while shortward of the Lyman limit it is 1.2-1.7. The energy distribution in ν f ν therefore peaks close to the quasar Lyman limit. The short wavelength UV power law extrapolates well to match the soft X-ray excess seen in simultaneous observations with the Broad-Band X-ray Telescope and nearly simultaneous ROSAT observations.The general shape of the broad-band spectrum of 3C 273 is consistent with that of an optically thick accretion disk whose emergent spectrum has been Comptonized by a hot medium. Our UV spectrum is well described by a Schwarzschild black hole of 7 × 10 8 M ⊙ accreting matter at a rate of 13 M ⊙ yr −1 through a disk inclined at 60 degrees. Superposed on the intrinsic disk spectrum is an empirically determined Lyman edge of optical depth 0.5. The Comptonizing medium has a Compton parameter y ≈ 1, obtained with an optical depth to electron scattering of unity and a temperature of 4 × 10 8 K.This overall shape is the same as that found by Zheng et al. and Laor et al. in their UV and X-ray composite spectra for quasars, giving physical validity to the composite spectrum approach. When combined with those results, we find that the generic ionizing continuum shape for quasars is a power law of energy index 1.7-2.2, extending from the Lyman limit to ∼1 keV. The observational gap in the extreme ultraviolet for these combined data describing the quasar continuum shape is now only half a decade in frequency.

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the selfconfiguration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.

A neural network solving Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. Database created to optimize the neural network's free parameters contain off-line EFIT results as the output of the network from 1, 118 KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks fully reconstruct not only the poloidal flux function ψ (R, Z) but also the toroidal current density function j φ (R, Z) with the off-line EFIT quality. To preserve robustness of the networks against a few missing input data, an imputation scheme is utilized to eliminate the required additional training sets with large number of possible combinations of the missing inputs.

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