deep learning algorithm

Sewage flow path is the main component of urban sewerage network infrastructure. Damage to sewers is less noticeable due to invisibility, and this failure to handle the damage leads to emergencies and unreasonable costs. These vital arteries need to be maintained and rebuilt during service for optimal performance in all dimensions. Nowadays, the methods of processing and classifying photos and videos taken by mobile videometer robots are widely used to inspect the sewer network. One of the successful algorithms in the field of image processing is the convolutional neural network algorithm, which is a subset of deep learning algorithm. In this paper, a convolutional neural network algorithm is used to classify images of sewer network damage and cases affecting the improvement, accuracy and performance of this algorithm. The images were obtained by a videometric robot from the sewer network. Results of using the proposed algorithm in the sewerage network, achieving 98% accuracy in classifying network faults and compared to other methods and also reducing the relatively low execution time of the proposed architecture (91 minutes) compared to other architectures valid ones are the same in deep learning on the same hardware platform. Also, in the future, the proposed algorithm will be used to analyze networks without the need for specialized personnel and also to control an automatic network videometry robot.

Automatic classification of power quality disturbances is the foundation to deal with the power quality problem. From a traditional viewpoint, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection, and classification. However, there are some inherent defects in signal analysis and the procedure of manual feature selection is tedious and imprecise, leading to a low classification accuracy of multiple disturbances. To deal with these problems, this paper presents an automated system for the classification and identification of power quality disturbances. After receiving input signals, the proposed system requires some preprocessing such as changing the range of values by dividing the signals into their basic domains. In the next stage, the RMS value of the signal can be appraised to know the occurrence of the disturbance. If the RMS value of the input signal is not equal to the normal signal, the disturbance is occurring. To identify and classify disturbances, a novel deep learning-based method is developed. In this method, the activation function is expressed by a fuzzy approach. This makes the system more flexible. The benefits of the proposed strategy are separating the disturbances of basic frequency and using the nature of power quality signals as a tool for feature extraction. However, in the traditional method, for example, in empirical mode decomposition, the separation of signals from their components is not conveniently possible. To evaluate the proposed algorithm, a 33-bus distribution power network has been applied. The results reveal good agreement in comparison with other assessment tests.