Wind Electronic Generator NU-101 Driven by Axial-Flow Air-Turbine with Stator

"Generators driven by axial-flow air-turbine with stator are relatively small in size and can be adapted to withstand violent blizzards. They are suitable for antarctic use, especially as energy sources for unmanned observation units. An analysis is given of the performance of an axial-flow air-turbine comprising a stator having stationary blades and a rotor having moving blades. Changes in the velocity and thermodynamic properties of the air flow in passing through the turbine are discussed quantitatively, and the expression for the output power and efficiencies of the turbine are given. It is shown that the output attains its maximum when the axial velocity of the air flow in the turbine is 1/√ of the wind velocity. The method of evaluation of the maximum annual wind energy available is discussed. The design of turbine blades is described: the constant nozzle angle design is adopted for the stationary blades, and the axially leaving velocity design for the moving blades. The starting torque, the starting wind velocity, and the off- design performance of the wind air-turbine are also studied. The performance of an air-turbine with stator is compared theoretically to that of an ordinary wind air-turbine without stator, and it is verified the maximum output power allowable for the former will be about 140% of the latter at a lower rotational speed than the latter, which will be about 70% thereof. A wind electric generator designated NU-101 which has been designed on the basis of the discussion outlined above is described. This comprises an axial-flow air-turbine, 1.2m in diameter, whose maximum rotational velocityis 300 RPM, and a 2 kW, 100V, AC generator. In 1972, this generator wasshipped to Syowa Station, Antarctica, by the 14th Japanese Antarctic ResearchExpedition (JARE). The results of some test runs in Tokyo in 1972 and atSyowa Station in 1973 are given.

Development and evaluation of a self care program on breastfeeding in Japan: A quasi-experimental study

金沢大学医薬保健研究域保健学系Background: Although the importance of breastfeeding is well known in Japan, in recent years less than 50% of mothers were fully breastfeeding at one month after birth. The purpose of this study was to develop a self-care program for breastfeeding aimed at increasing mothers\u27 breastfeeding confidence and to evaluate its effectiveness.Methods: A quasi-experimental pretest-posttest design was conducted in Japan. The intervention, a breastfeeding self-care program, was created to improve mothers\u27 self-efficacy for breastfeeding. This Breastfeeding Self-Care Program included: information on the advantages and basics of breastfeeding, a breastfeeding checklist to evaluate breastfeeding by mothers and midwives, and a pamphlet and audiovisual materials on breastfeeding. Mothers received this program during their postpartum hospital stay.A convenience sample of 117 primiparous women was recruited at two clinical sites from October 2007 to March 2008. The intervention group (n = 55), who gave birth in three odd-numbered months, received standard care and the Breastfeeding Self-Care Program while the control group (n = 62) gave birth in three even numbered months and received standard breastfeeding care.To evaluate the effectiveness of the Breastfeeding Self-Care Program, breastfeeding self-efficacy and breastfeeding rate were measured early postpartum, before the intervention, and after the intervention at one month postpartum. The study used the Japanese version of The Breastfeeding Self-Efficacy Scale Short Form (BSES-SF) to measure self-efficacy.Results: The BSES-SF score of the intervention group rose significantly from 34.8 at early postpartum to 49.9 at one month after birth (p < 0.01). For the control group, the score rose from 39.5 at early postpartum to 46.5 at one month after birth (p = 0.03). The early postpartum fully breastfeeding rate was 90% for the intervention group and 89% for the control group. At one month postpartum, the fully breastfeeding rate declined significantly to 65% for the control group compared to 90% for the intervention group (p = 0.02).Conclusion: Results indicate that the Breastfeeding Self-Care Program increased mothers\u27 self-efficacy for breastfeeding and had a positive effect on the continuation of breastfeeding. © 2010 Awano and Shimada; licensee BioMed Central Ltd

B²N²: Resource efficient Bayesian neural network accelerator using Bernoulli sampler on FPGA

A resource efficient hardware accelerator for Bayesian neural network (BNN) named B²N², Bernoulli random number based Bayesian neural network accelerator, is proposed. As neural networks expand their application into risk sensitive domains where mispredictions may cause serious social and economic losses, evaluating the NN’s confidence on its prediction has emerged as a critical concern. Among many uncertainty evaluation methods, BNN provides a theoretically grounded way to evaluate the uncertainty of NN’s output by treating network parameters as random variables. By exploiting the central limit theorem, we propose to replace costly Gaussian random number generators (RNG) with Bernoulli RNG which can be efficiently implemented on hardware since the possible outcome from Bernoulli distribution is binary. We demonstrate that B²N² implemented on Xilinx ZCU104 FPGA board consumes only 465 DSPs and 81661 LUTs which corresponds to 50.9% and 14.3% reductions compared to Gaussian-BNN (Hirayama et al., 2020) implemented on the same FPGA board for fair comparison. We further compare B²N² with VIBNN (Cai et al., 2018), which shows that B²N² successfully reduced DSPs and LUTs usages by 50.9% and 57.9%, respectively. Owing to the reduced hardware resources, B²N² improved energy efficiency by 7.50% and 57.5% compared to Gaussian-BNN (Hirayama et al., 2020) and VIBNN (Cai et al., 2018), respectively

Efficient Aging-aware Failure Probability Estimation Using Augmented Reliability and Subset Simulation

A circuit-aging simulation that efficiently calculates temporal change of rare circuit-failure probability is proposed. While conventional methods required a long computational time due to the necessity of conducting separate calculations of failure probability at each device age, the proposed Monte Carlo based method requires to run only a single set of simulation. By applying the augmented reliability and subset simulation framework, the change of failure probability along the lifetime of the device can be evaluated through the analysis of the Monte Carlo samples. Combined with the two-step sample generation technique, the proposed method reduces the computational time to about 1/6 of that of the conventional method while maintaining a sufficient estimation accuracy