Active vibration control of the first three modes of a vibrating cantilever beam using collocated piezoelectric sensor and actuator is examined in this paper. To achieve this, a model based on Euler-Bernoulli beam equation is adopted and extended to the case of three bonded piezoelectric patches that act as sensor, actuator and exciter respectively. A compensated inverse PID controller has been designed and developed to damp first three modes of vibration. Controllers have been designed for each mode and these are later combined in parallel to damp any of the three modes. Individual controller gives better reduction in sensor output for the second and third modes while the combined controller performs better for the first mode. Simulation studies are carried out using MATLAB. These results are compared and verified experimentally and the real-time implementation is carried out with xPC-target toolbox in MATLAB
This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%.
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