We propose a dynamic simultaneous localization and mapping technology for unsupervised motion removal (UMR-SLAM), which is a deep learning-based dynamic RGBD SLAM. It is the first time that a scheme combining scene flow and deep learning SLAM is proposed to improve the accuracy of SLAM in dynamic scenes, in response to the situation where dynamic objects cause pose changes. The entire process does not require explicit object segmentation as supervisory information. We also propose a loop detection scheme that combines optical flow and feature similarity in the backend optimization section of the SLAM system to improve the accuracy of loop detection. UMR-SLAM is rewritten based on the DROID-SLAM code architecture. Through experiments on different datasets, it has been proven that our scheme has higher pose accuracy in dynamic scenarios compared with the current advanced SLAM algorithm.

A distributed information network with complex network structure always has a challenge of locating fault root causes. In this paper, we propose a novel root cause analysis (RCA) method by random walk on the weighted fault propagation graph. Different from other RCA methods, it mines effective features information related to root causes from offline alarms. Combined with the information, online alarms and graph relationship of network structure are used to construct a weighted graph. Thus, this approach does not require operational experience and can be widely applied in different distributed networks. The proposed method can be used in multiple fault location cases. The experiment results show the proposed approach achieves much better performance with 6% higher precision at least for root fault location, compared with three baseline methods. Besides, we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.

Edge computation offloading allows mobile end devices to execute compute-intensive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally according to current network condition and devices’ profiles in an online manner. In this paper, we propose an edge computation offloading framework based on deep imitation learning (DIL) and knowledge distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online. We formalize a computation offloading problem into a multi-label classification problem. Training samples for our DIL model are generated in an offline manner. After the model is trained, we leverage KD to obtain a lightweight DIL model, by which we further reduce the model’s inference delay. Numerical experiment shows that the offloading decisions made by our model not only outperform those made by other related policies in latency metric, but also have the shortest inference delay among all policies.