Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Recent advances in nanorobotic manipulation inside scanning electron microscopes.

Abstract: A scanning electron microscope (SEM) provides real-time imaging with nanometer resolution and a large scanning area, which enables the development and integration of robotic nanomanipulation systems inside a vacuum chamber to realize simultaneous imaging and direct interactions with nanoscaled samples. Emerging techniques for nanorobotic manipulation during SEM imaging enable the characterization of nanomaterials and nanostructures and the prototyping/assembly of nanodevices. This paper presents a comprehensive survey of recent advances in nanorobotic manipulation, including the development of nanomanipulation platforms, tools, changeable toolboxes, sensing units, control strategies, electron beam-induced deposition approaches, automation techniques, and nanomanipulation-enabled applications and discoveries. The limitations of the existing technologies and prospects for new technologies are also discussed.

FMEA Using Uncertainty Theories and MCDM Methods

Abstract: To resolve the shortcomings of the conventional RPN method, a great number of studies have been conducted on the improvement of FMEA and a variety of alternative approaches have been proposed.

A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis

Abstract: Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2-tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C-arm X-ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.

Watermarking With Flexible Self-Recovery Quality Based on Compressive Sensing and Compositive Reconstruction

Abstract: This paper proposes a novel watermarking scheme with flexible self-recovery quality. The embedded watermark data for content recovery are calculated from the original discrete cosine transform (DCT) coefficients of host image and do not contain any additional redundancy. When a part of a watermarked image is tampered, the watermark data in the area without any modification still can be extracted. If the amount of extracted data is large, we can reconstruct the original coefficients in the tampered area according to the constraints given by the extracted data. Otherwise, we may employ a compressive sensing technique to retrieve the coefficients by exploiting the sparseness in the DCT domain. This way, all the extracted watermark data contribute to the content recovery. The smaller the tampered area, the more available watermark data will result in a better quality of recovered content. It is also shown that the proposed scheme outperforms previous techniques in general.