Biblio

Filters: Author is Bai, L.  [Clear All Filters]
2020-12-17
Hu, Z., Niu, J., Ren, T., Li, H., Rui, Y., Qiu, Y., Bai, L..  2020.  A Resource Management Model for Real-time Edge System of Multiple Robots. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :222—227.

Industrial robots are playing an important role in now a day industrial productions. However, due to the increasing in robot hardware modules and the rapid expansion of software modules, the reliability of operating systems for industrial robots is facing severe challenges, especially for the light-weight edge computing platforms. Based on current technologies on resource security isolation protection and access control, a novel resource management model for real-time edge system of multiple robot arms is proposed on light-weight edge devices. This novel resource management model can achieve the following functions: mission-critical resource classification, resource security access control, and multi-level security data isolation transmission. We also propose a fault location and isolation model on each lightweight edge device, which ensures the reliability of the entire system. Experimental results show that the robot operating system can meet the requirements of hierarchical management and resource access control. Compared with the existing methods, the fault location and isolation model can effectively locate and deal with the faults generated by the system.

2021-02-15
Liang, Y., Bai, L., Shao, J., Cheng, Y..  2020.  Application of Tensor Decomposition Methods In Eddy Current Pulsed Thermography Sequences Processing. 2020 International Conference on Sensing, Measurement Data Analytics in the era of Artificial Intelligence (ICSMD). :401–406.
Eddy Current Pulsed Thermography (ECPT) is widely used in Nondestructive Testing (NDT) of metal defects where the defect information is sometimes affected by coil noise and edge noise, therefore, it is necessary to segment the ECPT image sequences to improve the detection effect, that is, segmenting the defect part from the background. At present, the methods widely used in ECPT are mostly based on matrix decomposition theory. In fact, tensor decomposition is a new hotspot in the field of image segmentation and has been widely used in many image segmentation scenes, but it is not a general method in ECPT. This paper analyzes the feasibility of the usage of tensor decomposition in ECPT and designs several experiments on different samples to verify the effects of two popular tensor decomposition algorithms in ECPT. This paper also compares the matrix decomposition methods and the tensor decomposition methods in terms of treatment effect, time cost, detection success rate, etc. Through the experimental results, this paper points out the advantages and disadvantages of tensor decomposition methods in ECPT and analyzes the suitable engineering application scenarios of tensor decomposition in ECPT.