rongsheng gong state street factory

Alexander Otto owns a stake in Otto Group and is majority shareholder and CEO of ECE Group, a commercial real estate firm focused on shopping centers.

Khan, A., Li, R. J., Sun, J. T., Ma, F., Zhang, H. X., Jin, J. H., Ali, M., Haq, S. U., Wang, J. E., and Gong, Z. H. 2018. Genome-wide analysis of dirigent gene family in pepper (Capsicum annuum L.) and characterization of CaDIR7 in biotic and abiotic stresses. Sci. Rep. 8:5500-5520. https://doi.org/10.1038/s41598-018-23761-0 Crossref, Medline, ISI

Pietrzykowska, M., Suorsa, M., Semchonok, D. A., Tikkanen, M., Boekema, E. J., Aro, E.-M., and Jansson, S. 2014. The light-harvesting chlorophyll a/b binding proteins Lhcb1 and Lhcb2 play complementary roles during state transitions in Arabidopsis. Plant Cell 26:3646-3660. https://doi.org/10.1105/tpc.114.127373 Crossref, Medline, ISI

Faster R-CNN is a state-of-the-art method for detecting objects with real-time object detection, which can generate regions of interest (ROIs) with an RPN instead of selective search [197, 211]. Lei et al. [211] adopted Faster R-CNN to implement the detection of defects in the polarizer and to perform the rapid detection and effective positioning of defects. To further improve the detection accuracy and efficiency, the number of layers of the network could be changed, and some of the network parameters should be adjusted to optimize the test model. Lei and Sui [212] proposed a Faster R-CNN method to perform intelligent fault detection for high voltage lines. To detect defects in an image, Faster R-CNN chooses a random region as the proposal region and then obtains the corresponding category and location of a certain component after training. The experiments showed that the detection method based on the ResNet-101 network model could effectively locate insulator damage and bird nests on a high voltage line. Sun et al. [213] proposed an improved Faster R-CNN method for surface defect recognition in wheel hubs. The last maximum pooling layer was replaced by an ROI pooling layer, as shown in Fig. 8. ROI pooling technology was used in order to employ a single feature map for all the proposals generated by the RPN in a single pass. It enabled object detection networks to use an input feature map with a flexible size and output a fixed-size feature map. The experimental results showed that the improved Faster R-CNN method has a higher detection accuracy. However, the detection speed of the Faster R-CNN method may not meet the real-time requirements of industrial applications.

FCN-based segmentation methods also play an important role in industrial applications. Yu et al. [253] presented a novel 2-stage FCN framework for surface defect segmentation. The 2-stage framework improves the generality and reusability of FCNs. Li et al. [254] adopted region-based fully convolutional networks (R-FCNs) to inspect insulator defects. The experimental results showed that the R-FCN algorithm has good robustness and environmental adaptability. In crack inspection, conventional approaches are unable to identify and measure diverse types of cracks concurrently at the pixel level. Yang et al. [255] applied an FCN to study automatic pixel-level crack detection and measurement, and their results showed that the prediction had improved at the pixel level and that the training time was greatly reduced. However, the resolution of the feature maps generated by the FCN was low, and the prediction results were coarse owing to the large amount of spatial information loss during down-sampling. Qiu et al. [256] presented a 3-stage FCN for pixelwise surface defect segmentation. The FCN is a state-of-the-art algorithm for generic object segmentation. However, for small datasets, its performance cannot meet the requirements. The experimental results showed that the slicing method could improve the efficiency of FCNs in small datasets in industrial environments.

In the past 20 years, a range of planar and tubular solid oxide fuel cells (SOFCs) have been developed, using conventional ceramic processing, including tape-casting, extrusion, and screen-printing, with multiple intervening high-temperature processes. While multiple commercial players offer such SOFC products, successful commercialization remains hindered by the capital investment necessary to scale fabrication, and the concomitant high manufacturing cost of SOFC cells, stacks and systems. The oxide materials of construction constrain state-of-the-art cells fabrication, requiring process temperatures of 1200-1400°C for at least one process step. Subsequent functional layer deposition (e.g., electrolyte, interfacial, and electrode layers) requires temperatures of 900-1100°C. Ifmore »new manufacturing approaches could reduce cell cost and complexity, and provide performance improvements, the rate of SOFC adoption would be accelerated. Public success has created market-pull in applications that stretch SOFCs beyond their current capabilities. Such evolution is natural; the market has witnessed planar stack designs outstrip tubular designs in modular gas-powered solutions, and now the industry’s success in stationary markets has bred renewed interest in more challenging applications (e.g., Uninterruptible Power Supply, UPS systems for data centers and vehicle auxiliary power units, APUs). These applications require performance improvements over the best planar stacks, to achieve targeted levels of mechanical robustness, thermal cyclability, redox tolerance and power density. Such enticing markets may well be attainable with refinement of new, lower-cost metal supported cell designs, but the window for development is short. Current SOFC design cycles are too long and use fabrication approaches that are too difficult to tailor for meaningful improvement. SOFC developers and their customers need better tools and faster approaches to build and test prototype designs. In Phase I SBIR/STTR, Nexceris and West Virginia University (WVU) developed high power density metal-supported solid oxide fuel cells (MS-SOFCs) using additive manufacturing approach that allows design and manufacture of cells with unique electrode and electrolyte architectures at rapid pace (unachievable by conventional methods of tape casting and screen printing) at sizes required for application-specific testing. The process and new cell designs are also suitable for automated manufacturing at commercial scales. The cells incorporate thin metal and ceramic layers with controlled microstructure and interfaces. These cells offer lower material costs, by substituting inexpensive porous metal for a NiO/zirconia mechanical support layers (more than 80% of the cell volume), higher performance than state-of-the-art metal-supported SOFCs, and robustness towards thermal and redox cycling while being able to be started rapidly upon demand. Excellent reproducibility and cell performance has been achieved in such cells particularly at T ≤ 750°C. In parallel, additive manufacturing offers new opportunities to build complex layered structures, with excellent compositional and microstructural control. A key design feature of high performance SOFCs is the anode/electrolyte/cathode interfaces. 3D printing offers the means to control the composition and microstructure of these regions.« less