Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

A Survey of Embedding Algorithm for Virtual Network Embedding

Network virtualization(NV) is pushed forward by its proponents as a crucial attribute of next generation network, aiming at overcoming the gradual ossification of current networks, particularly to the worldwide Internet. Through virtualization, multiple customized virtual networks(VNs), requested by users, are allowed to coexist on the underlying substrate networks(SNs). In addition, the virtualization scheme contributes to sharing underlying physical resources simultaneously and seamlessly. However, multiple technical issues still stand in the way of NV successful implementation. One key technical issue is virtual network embedding(VNE), known as the resource allocation problem for NV. This paper conducts a survey of embedding algorithms for VNE problem. At first, the NV business model for VNE problem is presented. Then, the latest VNE problem description is presented. Main performance metrics for evaluating embedding algorithms are also involved. Afterwards, existing VNE algorithms are detailed, according to the novel proposed category approach. Next, key future research aspects of embedding algorithms are listed out. Finally, the paper is briefly concluded.

Multiple Object Tracking through Background Learning

This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance.

Tree Detection Algorithm Based on Embedded YOLO Lightweight Network

To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17%and 69.91%average precision values respectively for trunk and spherical tree,and 77.04% mean average precision value.The number of convolution parameters is 1.78×10^(6),and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.

Task Graph Reduction Algorithm for Hardware/Software Partitioning

Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with previous research that focuses on developing efficient heuristic,we focus on the pre-process of the task graph before the HW/SW partitioning in this paper,that is,enumerating all the sub-graphs that meet the requirements.Experimental results showed that the original graph can be reduced to 67% in the worst-case scenario and 58% in the best-case scenario.In conclusion,the reduced task graph saved hardware area while improving partitioning speed and accuracy.

A new neural network algorithm for planarization problems

To deal with the planarization problem widely used in many applications including routing very-large-scale integration （VLSI） circuits, this paper points out that only when its vertices are arranged in some specific order in a line can a planar graph be embedded on a line without any cross connections or cross edges. Energy function is proposed to meet the need of embedding a graph on a single line and route it correctly. A Hopfield network is designed according to the proposed energy function for such embedding and routing. The advantage of the proposed method is that it not only can detect if a graph is a planar one or not, but also can embed a planar graph or the maximal planar subgraph of a non-planar graph on a single line. In addition, simulated annealing is employed for helping the network to escape from local minima during the running of the Hopfield network. Experiments of the proposed method and its comparison with some existent conventional methods were performed and the results indicate that the proposed method is of great feasibility and effectiveness especially for the planarization problem of large graphs.