Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.

This study presents a multimodal human-exoskeleton cooperative control method to realize different control modes smoothly switching each other with satisfactory stable performance. Considering existed mismatch gaits of the operator comparison with the exoskeleton, the corresponding operator's gait is planned by central pattern generators (CPGs) to reduce human-exoskeleton impedance and generate real-time desired trajectory, which are used as the trajectory demand input of the exoskeleton control. Then, the admittance modulation factors is proposed to realize three motion control modes of lower limb exoskeleton, i.e., active, passive, and assist-as-needed. Meanwhile, an adaptive backstepping controller with the radial basis function neyral network estimation law is designed to guarantee the position tracking errors in uniformly ultimately boundedness under model uncertainty. Finally, the experimental studies are performed with an able-bodied operator by regulating the CPGs model parameters and modulation factors to verify the proposed multimodal human-exoskeleton cooperative control.

In this paper, a method of characteristic extraction and recognition on lung sounds is given. Wavelet de-noised method is adopted to reduce noise of collected lung sounds and extract wavelet characteristic coefficients of the de-noised lung sounds by wavelet decomposition. Considering the problem that lung sounds characteristic vectors are of high dimensions after wavelet decomposition and reconstruction, a new method is proposed to transform the characteristic vectors from reconstructed signals into reconstructed signal energy. In addition, we use linear discriminant analysis (LDA) to reduce the dimension of characteristic vectors for comparison in order to obtain a more efficient way for recognition. Finally, we use BP neural network to carry out lung sounds recognition where comparatively high-dimensional characteristic vectors and low- dimensional vectors are set as input and lung sounds categories as output with a recognition accuracy of 82.5% and 92.5%.