Effects of blunt trailing-edge optimization on aerodynamic characteristics of NREL phase VI wind turbine blade under rime ice conditions

To reduce the adverse effects of the ice on aerodynamic characteristics, a new NREL Phase VI wind turbine blade which is suitable to rime ice environments is developed through the blunt trailing-edge optimization. The parametric control equations of blunt trailing-edge airfoil are established by adopting the airfoil profile integration theory and B-spline curve, and the curve fitting of the airfoil’s rime ice from LEWICE software is carried out using the linear interpolation algorithm with equidistant and equiangular step lengths. The S809 airfoil under rime ice conditions is optimized to maximize the lift coefficient by the particle swarm optimization (PSO) coupled with GAMBIT and FLUENT, and a NREL Phase VI blade is formed with the optimized airfoil S809-BT (with BT the blunt trailing-edge). The blade’s rime ice is obtained through using the polynomial fitting to deal with projection point coordinates of airfoils’ ice shapes in lagging and flapping surfaces, and the pressure coefficient, flow characteristics, torque and output power of icy sharp and blunt trailing-edge blades are investigated. The results indicate that in rime ice conditions, compared with those of sharp trailing-edge blade, the pressure difference and vortex size of blunt trailing-edge blade become larger, and the torque and output power increase by 4.36 %, 1.55 % and 2.88 % at v= 7 m/s, 15 m/s and 20 m/s, respectively. The research provides significant guidance for improving the aerodynamic performance of wind turbine blade considering the icing effects

RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN

The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the second-order information (SOI) matrix to find a more accurate descent direction and step size. However, the huge SOI matrices bring significant computational and memory overheads in the traditional architectures like GPU and CPU. On the other side, the ReRAM-based process-in-memory (PIM) technology is suitable for the second-order training because of the following three reasons: First, PIM's computation happens in memory, which reduces data movement overheads; Second, ReRAM crossbars can compute SOI's inversion in $O\left(1\right)$ time; Third, if architected properly, ReRAM crossbars can perform matrix inversion and vector-matrix multiplications which are important to the second-order training algorithms. Nevertheless, current ReRAM-based PIM techniques still face a key challenge for accelerating the second-order training. The existing ReRAM-based matrix inversion circuitry can only support 8-bit accuracy matrix inversion and the computational precision is not sufficient for the second-order training that needs at least 16-bit accurate matrix inversion. In this work, we propose a method to achieve high-precision matrix inversion based on a proven 8-bit matrix inversion (INV) circuitry and vector-matrix multiplication (VMM) circuitry. We design \archname{}, a ReRAM-based PIM accelerator architecture for the second-order training. Moreover, we propose a software mapping scheme for \archname{} to further optimize the performance by fusing VMM and INV crossbar. Experiment shows that \archname{} can achieve an average of 115.8$\times$/11.4$\times$ speedup and 41.9$\times$/12.8$\times$energy saving compared to a GPU counterpart and PipeLayer on large-scale DNNs.Comment: 13pages, 13 figure

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Improving "color rendering" of LED lighting for the growth of lettuce

Light plays a vital role on the growth and development of plant. On the base of white light with high color rendering to the benefit of human survival and life, we proposed to improve “color rendering” of LED lighting for accelerating the growth of lettuce. Seven spectral LED lights were adopted to irradiate the lettuces under 150 μmol·m−2·s−1 for a 16 hd−1 photoperiod. The leaf area and number profiles, plant biomass, and photosynthetic rate under the as-prepared LED light treatments were investigated. We let the absorption spectrum of fresh leaf be the emission spectrum of ideal light and then evaluate the “color rendering” of as-prepared LED lights by the Pearson product-moment correlation coefficient and CIE chromaticity coordinates. Under the irradiation of red-yellow-blue light with high correlation coefficient of 0.587, the dry weights and leaf growth rate are 2-3 times as high as the sharp red-blue light. The optimized LED light for lettuce growth can be presumed to be limited to the angle (about 75°) between the vectors passed through the ideal light in the CIE chromaticity coordinates. These findings open up a new idea to assess and find the optimized LED light for plant growth

Interaction between electrical storm and left ventricular ejection fraction as predictors of mortality in patients with implantable cardioverter defibrillator: A Chinese cohort study

AimsTo determine the interaction of electrical storm (ES) and impaired left ventircular ejection fraction (LVEF) on the mortality risk of patients with implantable cardioverter defibrillator (ICD).Methods and resultsA total of 554 Chinese ICD recipients from 2010 to 2014 were retrospectively included and the mean follow-up was 58 months. The proportions of dilated cardiomyopathy and the hypertrophic cardiomyopathy were 26.0% (144/554) and 5.6% (31/554), respectively. There were 8 cases with long QT syndrome, 6 with arrhythmogenic right ventricular cardiomyopathy and 2 with Brugada syndrome. Patients with prior MI accounted for 15.5% (86/554) and pre-implantation syncope accounted for 23.3% (129/554). A total of 199 (35.9%) patients had primary prevention indications for ICD therapy. Both ES and impaired LVEF (<40%) were independent predictors for all-cause mortality [hazard ratio (HR) 2.40, 95% CI 1.57–3.68, P < 0.001; HR 1.94, 95% CI 1.30–2.90, P = 0.001, respectively] and cardiovascular mortality (HR 4.63, 95% CI 2.68–7.98, P < 0.001; HR 2.56, 95% CI 1.47–4.44, p = 0.001, respectively). Compared with patients with preserved LVEF (≥40%) and without ES, patients with impaired LVEF and ES had highest all-cause and cardiovascular mortality risks (HR 4.17, 95% CI 2.16–8.06, P < 0.001; HR 11.91, 95% CI 5.55–25.56, P < 0.001, respectively). In patients with impaired LVEF, ES increased the all-cause and cardiovascular mortality risks (HR 1.84, 95% CI 1.00–3.37, P = 0.034; HR 4.86, 95% CI 2.39–9.86, P < 0.001, respectively). In patients with ES, the deleterious effects of impaired LVEF seemed confined to cardiovascular mortality (HR 2.54, 95% CI 1.25–5.14, p = 0.038), and the HR for all-cause mortality was not significant statistically (HR 1.14, 95% CI 0.54–2.38, P = 0.735).ConclusionBoth ES and impaired LVEF are independent predictors of mortality risk in this Chinese cohort of ICD recipients. The interaction of ES and impaired LVEF in patients significantly amplifies the deleterious effects of each other as distinct disease