Programmable Adaptive Security Scanning for Networked Microgrids

Communication-dependent and software-based distributed energy resources(DERs)are extensively integrated into modern microgrids,providing extensive benefits such as increased distributed controllability,scalability,and observability.However,malicious cyber-attackers can exploit various potential vulnerabilities.In this study,a programmable adaptive security scanning(PASS)approach is presented to protect DER inverters against various power-bot attacks.Specifically,three different types of attacks,namely controller manipulation,replay,and injection attacks,are considered.This approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids(NMs)in an ultra-resilient,time-saving,and autonomous manner.The coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM operations.Extensive simulation results validate the efficacy and practicality of the PASS for securing NMs.

Coordinate scheduling approach for EDS observation tasks and data transmission jobs

Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.

Deep learning for the detection of semantic features in tree X-ray CT scans

According to the industry,the value of wood logs is heavily influenced by their internal structure,particularly the distribution of knots within the trees.Nowadays,CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees.Knowing where the tree semantic features,especially knots,contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products.However,this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline,knot localization and log contour,in a robust and efficient manner.In this paper,we propose an effective methodology based on deep learning for performing these different tasks by processing CTscanner images with deep convolutional neural networks.To meet this objective,three end-to-end trainable pipelines are proposed.The first pipeline is focused on centers detection using CNNs architecture with a regression head,the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture.The different architectures are tested on several tree species.With these experiments,we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness.The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.