Recent Advances in Doped Bi2O3 and its Photocatalytic Activity: A Review

Bismuth Oxide (Bi2O3) has a very promising photocatalytic ability to degrade waste pollutants under visible light irradiation because it has a small energy gap of around 2.85-2.58 eV. Although it has excellent potential as a photocatalyst, Bi2O3 has the disadvantage of a high electron-hole pair recombination rate, which will reduce its photocatalytic activity. To overcome these problems, surface modifications, defect recognition, or doping of Bi2O3 are carried out to obtain a more effective and efficient photocatalyst to degrade waste pollutants under visible light irradiation. Several studies by researchers have been described for the modification of Bi2O3 by doping. Various types of doping are given, such as doping in elements or doping in the form of compounds to form composites. Based on several studies that have been described, appropriate doping has been shown to increase the photocatalytic activity of Bi2O3. Keywords: Bi2O3, Photocatalyst, Doping

Defect Detection of Mechanical Design Products Using Deep Neural Network

In industrial production, defect detection is one of the key methods to control the quality of mechanical design products. Although defect detection algorithms based on traditional machine learning can greatly improve detection efficiency, manual feature extraction is required and the design process is complicated. With the rapid development of CNN, major breakthroughs have been made in computer vision. Therefore, building a surface defect detection algorithm for mechanical design products based on DCNNs plays a very important role in improving industrial production efficiency. This paper studies the surface defect detection algorithm of mechanical products based on deep convolutional neural network, focusing on solving two types of problems: defect recognition and defect segmentation. Aiming at the problem of defect recognition, this paper studies a defect recognition algorithm based on fully convolutional block detection. This algorithm introduces the idea of block detection into the ResNet fully convolutional neural network. While realizing the local discrimination mechanism, it overcomes the shortcomings of the traditional block detection receptive field. Compared with the original ResNet image classification algorithm, this algorithm has stronger generalization ability and detection ability of small defects. Aiming at the problem of defect segmentation, this paper studies a defect segmentation algorithm based on improved Deeplabv3+.

A Method for Image Recognition of Insulator Jacket Defects under Small Sample Conditions

Accurate identification of insulator jacket defect images requires a large number of samples for model training, and the actual defect image datasets available for model training is seriously insufficient. In order to solve the problems of the model cannot be trained, over-fitting and low accuracy caused by too few training samples, this paper proposes a new method for image recognition of insulator jacket defects under small sample conditions, which combines image enhancement technology and meta-learning technology to train the U-Net image segmentation network, and finally obtain the image recognition model of the insulator jacket defect. In this paper, the defect recognition models using meta-learning method and without meta-learning are compared experimentally, and the results show that the proposed method can achieve accurate recognition with a small-scale original data set.

An Intelligence Method for Recognizing Multiple Defects in Rail

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.

Image Recognition Technology with Its Application in Defect Detection and Diagnosis Analysis of Substation Equipment

Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Traditional manual diagnosis methods are difficult to deal with the massive infrared images generated by the autonomous inspection of substation robots and drones. At present, most of the infrared image defect recognition is based on traditional machine learning algorithms, with low recognition accuracy and poor generalization capability. Therefore, this paper develops a method for identifying infrared defects of substation equipment based on the improvement of traditional ones. First, based on the Faster RCNN, target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment. Afterwards, different classes are identified based on the sparse representation-based classification (SRC), so the actual label of the input sample can be obtained. Finally, based on the temperature threshold discriminant algorithm, defects are identified in the equipment area. The measured infrared images are used for experiments. The average detection accuracy achieved by the proposed method for the 6 types of equipment reaches 92.34%. The recognition rate of different types of equipment is 98.57%, and the defect recognition accuracy reaches 88.75%. The experimental results show the effectiveness and accuracy of the proposed method.