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The reference optical spectrum (ROS) based in-band optical signal-to-noiseratio (OSNR) monitoring methods are accurate and inherently robust to fiber chromatic dispersion and polarization mode dispersion. They are also very simple and suitable to be deployed ubiquitously in the transmission system. In this paper, a ROS-based in-band OSNR monitoring method is proposed for erbium doped fiber amplifier (EDFA) amplified multispan dense wavelength division multiplexing systems with cascaded filtering effect (CFE) caused by cascaded add-drop filters or wavelength selective switches. In such systems, the optical signal as well as the optical noise may experience significant CFE making the ROS-based methods proposed before ineffective. The new method solves this problem and performs very well for OSNR in the range of 10-30 dB when the fiber nonlinear effect and nonideal filtering effect are present. The OSNR monitor based on the new method is also more convenient to use as only one-time calibration is required when used at different monitoring locations along the optical link.

In order to realize less time consuming and on-line image classification for steel strip surface defects, an improved multiclass support vector machine (SVM) was proposed. The SVM used a novel algorithm and only constructed (k-1) two-class SVMs where K is the number of classes. In the testing phase, to identify the surface defects it used a new unidirectional acyclic graph which had internal (k-1) nodes and k leaves. Its testing time is less than traditional multiclass SVM method. The experiment results shows that this method is simple and less time consuming while preserving generalization ability and recognition accuracy toward steel strip surface defects.

Radiography inspection (X-ray or gamma ray) is one of the most commonly used Non-destructive Evaluation (NDE) methods. More and more digital X-ray imaging is used for medical diagnosis, security screening, or industrial inspection, which is important for e-manufacturing. In this paper, we firstly introduced an automatic welding defect inspection system for X-ray image evaluation, defect image database and applications of Artificial Neural Networks (ANNs) for NDE. Then, feature extraction and selection methods are used for defect representation. Seven categories of geometric features were defined and selected to represent characteristics of different kinds of welding defect. Finally, a feed-forward backpropagation neural network is implemented for the purpose of defect classification. The performance of the proposed methods are tested and discussed.

The tooth profile synthesis of swing movable teeth drives was performed. The tooth profile equation of the internal teeth ring was established. The analysis result shows the deformation distribution of the movable tooth in the normal direction at the contact point of the wave generator is a sine function. Furthermore, the forces of the wave generator, the movable tooth pin and the internal teeth ring acting on the movable tooth were analyzed. Based on the Hertz theory and material mechanics, the contact strength calculation methods for the meshing pairs of the movable tooth and the wave generator as well as the movable tooth and the internal teeth ring were discussed. The calculation method for the bending strength of the pin was also derived. The research result provides a theoretical basis for designing and applying swing movable teeth drives.