New developments in multibeam technology now permit MBES to collect and record acoustic data not only from the strongest return (normally the seabed), but also echo returns from the complete travel paths of the acoustic pulse through the water column. This now allows they are established as standard tools for the remote detection of targets in the water column, such as gas bubbles leaking from pipeline. In this study, a multibeam sonar operating at 300kHz is used to detect the gas leakage of pipeline based on acoustic backscatter imagery. Some behavioural traits of the leakage gas bubbles have been discussed, such as shape, distribution pattern and contour centroid characteristics. Firstly, an adaptive beamforming algorithm is applied to sonar imaging for suppressing background noise and side lobe interference. And then these features are extracted by mathematic morphological processing of image sequences. Finally, a tank test with different leakage scales caused by leakage pressures, amounts and sizes has verified the validity and stability of the characteristics of gas bubbles. The proposed method is feasible to make a qualitative assessment for AUV pipeline detection surveys.
Terrain-aided navigation (TAN) is a promising technique to determine the location of underwater vehicle by matching terrain measurement against a known map. The particle filter (PF) is a natural choice for TAN because of its ability to handle non-linear, multimodal problems. However, the terrain measurements are vulnerable to outliers, which will cause the PF to degrade or even diverge. Modification of the Gaussian likelihood function by using robust cost functions is a way to reduce the effect of outliers on an estimate. The authors propose to use the Huber function to modify the measurement model used to set importance weights in a PF. They verify their method in simulations of multi-beam sonar in a real underwater digital map. The results demonstrate that the proposed method is more robust to outliers than the standard PF (SPF).
Terrain-aided navigation is a promising approach to submerged position updates for autonomous underwater vehicles by matching terrain measurements against an underwater reference map. With an accurate prediction of tidal depth bias, a two-dimensional point mass filter, only estimating the horizontal position, has been proven to be effective for terrain-aided navigation. However, the tidal depth bias is unpredictable or predicts in many cases, which will result in the rapid performance degradation if a two-dimensional point mass filter is still used. To address this, a marginalized point mass filter in three dimensions is presented to concurrently estimate and compensate the tidal depth bias in this paper. In the method, the tidal depth bias is extended as a state variable and estimated using the Kalman filter, whereas the horizontal position state is still estimated by the original two-dimensional point mass filter. With the multibeam sonar, simulation experiments in a real underwater digital map demonstrate that the proposed method is able to accurately estimate the tidal depth bias and to obtain the robust navigation solution in suitable terrain.
Terrain matching positioning is a promising method to overcome the problem that the inertial navigation error of the underwater vehicle accumulates over time. In the conventional terrain matching method, all measurement points are commonly used for matching and positioning. However, this method fails to be taken into a balanced consideration on both the computation complexity and the positioning accuracy. To reduce the computation and ensure the accuracy at the same time, an improved terrain matching method based on the gradient fitting is proposed in this paper. In the method, the gradient distributions of multiple terrain regions are firstly analyzed. Then, normal distribution is used to fit them, and according to the distribution, points with larger gradient values are selected as matching points. Finally, minimum absolute difference matching is chosen to match for positioning. Simulation results using multibeam sonar show that the improved terrain matching localization method not only reduces the computational complexity but also improves the accuracy of positioning.
As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule− miRNA−cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular− miRNA−cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule−miRNA−cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful highthroughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/ LeeKamlong/Multi-class-SMMCA.
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