A Network Architecture with High Availability for Real-time Premium Traffic over the Internet

Real-time traffic will be a predominant traffic type in the next generation networks, and networks with 100% reliability and availability will be required by real-time premium traffic. It is believed that QoS guarantees could be better provided by connection oriented networks such as Multi Protocol Label Switching (MPLS). These connection oriented networks are more vulnerable to network failure. Conventional path protection methods perform re-routing to cope with this. However, re-routing always causes packet losses and results in service outage. These losses are bursty in nature and highly degrade the QoS of the real-time premium traffic. Thus, 100% availability cannot be achieved by conventional methods. The novel path protection proposed in this paper recovers the bursty packet losses due to re-routing by using forward error correction (FEC) path. Therefore, it can provide network architecture with no service outage for such traffic. The numerical results show that the proposed method can achieve a very high availability for real-time premium traffic in future IP/MPLS networks.

Bottom-up pyramid cellular acceptors with three-dimensional layers

In 1997, C.R. Dyer and A. Rosenfeld introduced an acceptor on a two-dimensional pattern (or tape), called the pyramid cellular acceptor, and demonstrated that many useful recognition tasks are executed by pyramid cellular acceptors in time proportional to the logarithm of the diameter of the input. They also introduced a bottom-up pyramid cellular acceptor which is a restricted version of the pyramid cellular acceptor, and proposed some interesting open problems of the bottom-up pyramid cellular acceptors. On the other hand, we think that the study of threedimensional automata has been meaningful as the computational model of three-dimensional information processing such as computer vision, robotics, and so forth. In this paper, we investigate about bottom-up pyramid cellular acceptors with three-dimensional layers, and show their some accepting powers. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Fatigue behaviour of precipitation-hardening medium C steels containing Cu

The presence of Cu precipitates counteracts the cyclic softening present in ordinary quenched and tempered steels. This is expected to result in an increase in fatigue limit. The fatigue crack propagation rate (dc/dN) at constant K in the Cu-C steels was shown to depend on heat-treatment and carbon content. To maximize yield strength and minimize ¦da/dN¦_K for tempering at 500° C, one must choose a low C content and temper for a short time; ¦da/dN¦_K in 0.28 wt % C-1.45 wt % Cu tempered for 13 min was one-third that for 0.45 wt % C-1.45 wt% Cu tempered for 200 min. There is also an advantage in adding Cu while simultaneously lowering the Ccontent. The dc/dN data are discussed in terms of the yield strength and the energy to form a unit area of fatigue crack, U, which was measured using foil strain gauges. The quantity (¦dc/dN ¦_{K y} ² U) where _y is the cyclic yield stress, was found to be nearly constant. In the 0.28 wt % C-1.45wt % Cu alloy, short ageing times at 500° C resulted in greater resistance to initiation of cracks at notches for low Ks than long ageing times.     

MOSAIC for multiple-reward environments

Reinforcement learning (RL) can provide a basic framework for autonomous robots to learn to control and maximize future cumulative rewards in complex environments. To achieve high performance, RL controllers must consider the complex external dynamics for movements and task (reward function) and optimize control commands. For example, a robot playing tennis and squash needs to cope with the different dynamics of a tennis or squash racket and such dynamic environmental factors as the wind. In addition, this robot has to tailor its tactics simultaneously under the rules of either game. This double complexity of the external dynamics and reward function sometimes becomes more complex when both the multiple dynamics and multiple reward functions switch implicitly, as in the situation of a real (multi-agent) game of tennis where one player cannot observe the intention of her opponents or her partner. The robot must consider its opponent's and its partner's unobservable behavioral goals (reward function). In this article, we address how an RL agent should be designed to handle such double complexity of dynamics and reward. We have previously proposed modular selection and identification for control (MOSAIC) to cope with nonstationary dynamics where appropriate controllers are selected and learned among many candidates based on the error of its paired dynamics predictor: the forward model. Here we extend this framework for RL and propose MOSAIC-MR architecture. It resembles MOSAIC in spirit and selects and learns an appropriate RL controller based on the RL controller's TD error using the errors of the dynamics (the forward model) and the reward predictors. Furthermore, unlike other MOSAIC variants for RL, RL controllers are not a priori paired with the fixed predictors of dynamics and rewards. The simulation results demonstrate that MOSAIC-MR outperforms other counterparts because of this flexible association ability among RL controllers, forward models, and reward predictors.     

High-intensity multi-bunch beam generation by a photo-cathode RF gun

A multi-bunch photo-cathode RF gun system has been developed as an electron source for the production of intense quasi-monochromatic X-rays based on inverse Compton scattering. The desired multi-bunch beam is 100 bunches/pulse with a total charge of 500 nC and a bunch spacing of 2.8 ns. We modified the gun cavity of a ‘BNL-type IV’ RF gun to allow a CsTe cathode plug in the end plate. The system uses a four-dipole chicane beam line to allow the injection of laser light normal to the cathode surface. We compensate the gun cavity beam loading caused by the high-intensity multi-bunch electron beam by injecting the laser pulse before RF power has filled the cavity. We have achieved a total intensity of 220 nC in 100 bunches with a bunch-to-bunch energy spread under 1.3% (peak-to-peak). This paper concentrates on experiments to generate the high-intensity multi-bunch beam with compensation of the bunch-to-bunch energy spread due to heavy beam loading.