Determination of Acetic Acid Content in Ethylene-Vinyl Acetate (EVA) Based PV Modules

[[abstract]]The acetic acid formation in EVA as one of the major products caused by thermal or photothermal degradation has been described in several papers.[1-3] Deacetylation and hydrolysis of vinyl-acetate monomers in EVA resulted in generating acetic acid that could accelerate the corrosion of electrical interconnects and deteriorate the transparency coating of the cells and then result in the eventual reduction in module performance.[4] Recently, ion chromatograph (IC), Infra-Red (IR) and Hot Water Extraction Method (HWEM) were used to evaluate the amount of free acetic acid desorbed in EVA.[3-5] In this study, more dedicated analytical technique of Thermal Desorption GC-MS was applied to quantitatively determine the acetic acid content within the full size PV modules with and without exposure. The correlation between the extent of acetic acid formation and the module performance was also investigated.[[conferencetype]]國際[[conferencedate]]20131028~20131101[[ispeerreviewed]]Y[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Generating Driving Scenes with Diffusion

In this paper we describe a learned method of traffic scene generation designed to simulate the output of the perception system of a self-driving car. In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel combination of diffusion and object detection to directly create realistic and physically plausible arrangements of discrete bounding boxes for agents. We show that our scene generation model is able to adapt to different regions in the US, producing scenarios that capture the intricacies of each region.Comment: Accepted to the ICRA Scalable Autonomous Driving Worksho

Spectral Transition and Torque Reversal in X-ray Pulsar 4U 1626-67

The accretion-powered, X-ray pulsar 4U 1626-67 has recently shown an abrupt torque reversal accompanied by a dramatic spectral transition and a relatively small luminosity change. The time-averaged X-ray spectrum during spin-down is considerably harder than during spin-up. The observed torque reversal can be explained by an accretion flow transition triggered by a gradual change in the mass accretion rate. The sudden transition to spin-down is caused by a change in the accretion flow rotation from Keplerian to sub-Keplerian. 4U 1626-67 is estimated to be near spin equilibrium with a mass accretion rate Mdot~2x10**16 g/s, Mdot decreasing at a rate ~6x10**14 g/s/yr, and a polar surface magnetic field of ~2b_p**{-1/2} 10^**12G where b_p is the magnetic pitch. During spin-up, the Keplerian flow remains geometrically thin and cool. During spin-down, the sub-Keplerian flow becomes geometrically thick and hot. Soft photons from near the stellar surface are Compton up-scattered by the hot accretion flow during spin-down while during spin-up such scattering is unlikely due to the small scale-height and low temperature of the flow. This mechanism accounts for the observed spectral hardening and small luminosity change. The scattering occurs in a hot radially falling column of material with a scattering depth ~0.3 and a temperature ~10^9K. The X-ray luminosity at energies >5keV could be a poor indicator of the mass accretion rate. We briefly discuss the possible application of this mechanism to GX 1+4, although there are indications that this system is significantly different from other torque-reversal systems.Comment: 10 pages, 1 figure, ApJ

Multi-Level Stochastic Gradient Methods for Nested Composition Optimization

Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions. Existing results mainly apply to optimization problems where the objectives are one- or two-level expectations. In this paper, we consider the multi-level compositional optimization problem that involves compositions of multi-level component functions and nested expectations over a random path. It finds applications in risk-averse optimization and sequential planning. We propose a class of multi-level stochastic gradient methods that are motivated from the method of multi-timescale stochastic approximation. First we propose a basic $T$-level stochastic compositional gradient algorithm, establish its almost sure convergence and obtain an $n$-iteration error bound $O (n^{-1/2^T})$. Then we develop accelerated multi-level stochastic gradient methods by using an extrapolation-interpolation scheme to take advantage of the smoothness of individual component functions. When all component functions are smooth, we show that the convergence rate improves to $O(n^{-4/(7+T)})$ for general objectives and $O (n^{-4/(3+T)})$ for strongly convex objectives. We also provide almost sure convergence and rate of convergence results for nonconvex problems. The proposed methods and theoretical results are validated using numerical experiments

SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections

In this work we propose a deep learning model, i.e., SAPI, to predict vehicle trajectories at intersections. SAPI uses an abstract way to represent and encode surrounding environment by utilizing information from real-time map, right-of-way, and surrounding traffic. The proposed model consists of two convolutional network (CNN) and recurrent neural network (RNN)-based encoders and one decoder. A refiner is proposed to conduct a look-back operation inside the model, in order to make full use of raw history trajectory information. We evaluate SAPI on a proprietary dataset collected in real-world intersections through autonomous vehicles. It is demonstrated that SAPI shows promising performance when predicting vehicle trajectories at intersection, and outperforms benchmark methods. The average displacement error(ADE) and final displacement error(FDE) for 6-second prediction are 1.84m and 4.32m respectively. We also show that the proposed model can accurately predict vehicle trajectories in different scenarios