Applications of muon absorption radiography to the fields of archaeology and civil engineering

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time-frequency representations in the form of mel spectrograms. Then, two image descriptors-the local binary pattern (LBP) and histogram of oriented gradient (HOG)-are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.

Muography: overview and future directions

Cosmic-ray muography uses high-energy particles for imaging applications that are produced by cosmic rays in particle showers in the Earth's atmosphere. This technology has developed rapidly over the last 15 years, and it is currently branching out into many different applications and moving from academic research to commercial application. As in any new sub-field of research and technology, the nomenclature of the field itself is still developing and has not settled yet as new aspects of the field are appearing and with them the terms to describe them. This overview of the field of muography is not going to focus on the physics, on the reconstruction algorithms or on the involved detector technology. Detailed papers on these aspects are included in this issue of Philosophical Transactions A and I will refer to them. Instead, I will give an overview of the field as it is now, in 2018, and try to give an idea of the future directions in this field as I see them.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.