A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains.

Prediction of foot placement presents great potential in better assisting the walking of people with lower-limb disability in daily terrains. Previous researches mainly focus on foot placement prediction in level ground walking, however these methods cannot be applied to daily complex terrains including ramps, stairs, and level ground with obstacles. To predict foot placement in complex terrains, this paper presents a probability fusion approach for foot placement prediction in complex terrains which consists of two parts: model training and foot placement prediction. In the first part, a deep learning model is trained on augmented data to predict the probability distribution of preliminary foot placement. In the second part, environmental information and human walking constraints are used to calculate the feasible area, and finally the feasible area is fused with the probability distribution of preliminary foot placement to predict the foot placement in complex terrains. The proposed method can predict the foot placement of next step in complex terrains when heel-off is detected. Experiments (including structured terrains experiments and complex terrains experiments) show that the root mean square error (RMSE) of prediction is 8.19 ± 1.20 cm, which is less than 8% of the average stride length, and the landing feasible area accuracy (LFAA) of prediction is 95.11 ± 3.09%. Comparing with existing foot placement prediction studies, the method proposed in this paper achieves faster and more accurate prediction in complex terrains.

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis.

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

Rapid and sensitive detection of Senecavirus A by reverse transcription loop-mediated isothermal amplification combined with a lateral flow dipstick method.

Senecavirus A (SVA) is a critical pathogen causing vesicular lesions in sows and acute death of newborn piglets, resulting in very large economic losses in the pig industry. To restrict the transmission of SVA, an establishment of an effective diagnostic method is crucial for the prevention and control of the disease. However, traditional detection methods often have many drawbacks. In this study, reverse transcription loop-mediated isothermal amplification (RT-LAMP) was combined with a lateral flow dipstick (LFD) to detect SVA. The resulting RT-LAMP-LFD assay was performed at 60°C for 50 min and then directly judged on an LFD visualization strip. This method shows high specificity and sensitivity to SVA. The detection limit of RT-LAMP was 4.56x10-8 ng/µL RNA, approximately 11 copies/µL RNA, and it was 10 times more sensitive than RT-PCR. This detection method's positive rate for clinical samples is comparable to that of RT-PCR. This method is time saving and highly efficient and is thus expected to be used to diagnose SVA infections in this field.

Complete Genome Sequence of Senecavirus A Strain SVV HN16 Identified in China.

A virus that caused blisters and ulcers in pigs in China was detected to be a strain of Senecavirus A (SVA). Complete genome sequencing and analysis results showed that the isolate shares 93.8% to 99.1% identity with other isolates that have been reported, proving that a new isolate of SVA was found in China.