Proactive Resilience Enhancement of Power Systems with Link Transmission Model-based Dynamic Traffic Assignment Among Electric Vehicles

The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilience of power systems towards extreme events.To quantify the temporal and spatial flexibility of EVs for charging and discharging,a novel dynamic traffic assignment(DTA)problem is proposed.The DTA problem is based on a link transmission model(LTM)with extended charging links,depicting the interaction between EVs and power systems.It models the charging rates as continuous variables by an energy boundary model.To consider the evacuation requirements of TNs and the uncertainties of traffic conditions,the DTA problem is extended to a two-stage distributionally robust version.It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs.The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers.Case studies are performed on two test networks.The effectiveness is verified by the numerical results,e.g.,reducing the load shedding amount without increasing the unmet traffic demand.

Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.

Energy-Efficient Routing Algorithm Based on Multipath Routing in Large-Scale Networks

A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is difficult.Therefore,this research investigates how to reduce network energy consumption without a real-time traffic matrix.In particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing.It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency.The main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network topologies.Experimental results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.

Assessing the impact on groundwater safety of inter-basin water transfer using a coupled modeling approach

Surface water and groundwater always behave in a coupled manner and are major components of hydrologic cycle. However, surface water simulation models and groundwater simulation models are run separately most of the time. Few models focus on the impact of hydraulic changes in the surface water flows on the groundwater, or specifically, the impact of a water transfer project to fill a seasonally dry channel. In this study, a linked surface water and groundwater simulation model was developed to assess the impact of a trans-basin water diversion project on the groundwater. A typical plain area east of Beijing was selected as a case study, representing Beijing＇s main source of groundwater used for drinking water. A surface water quality model of the Chaobai River was developed based on the Water Quality Analysis Simulation Program （WASP）, and a groundwater model was developed based on the Modular Finite- Difference Groundwater Flow Model （MODFLOW） and the Modular 3-D transport model （MT3D）. The results of the surface water simulation were used as input for the groundwater simulation. Water levels and four contaminants （NH3-N, CODMn, F, As） were simulated. With the same initial and boundary conditions, scenario analyses were performed to quantify the impact of different quantities of diversion water on the groundwater environment. The results showed the water quality of the groundwater sources was not significantly affected.

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.