A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.

3D Path prediction of moving objects in a video-augmented indoor virtual environment

Augmented virtual environments(AVE)combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously.AVE can provide solutions for continuous tracking of moving objects,camera scheduling,and path planning in the real world.This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment.The study includes 3D motion analysis of moving objects,multi-path prediction,hierarchical visualization,and path-based multi-camera scheduling.The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE.The path analysis algorithms proved accurate and time-efficient,costing less than 1.3 ms to get the optimal path.The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory,which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.

Statistical Model of Path Loss for Railway 5G Marshalling Yard Scenario

The railway mobile communication system is undergoing a smooth transition from the Global System for Mobile Communications-Railway(GSM-R)to the Railway 5G.In this paper,an empirical path loss model based on a large amount of measured data is established to predict the path loss in the Railway 5G marshalling yard scenario.According to the different characteristics of base station directional antennas,the antenna gain is verified.Then we propose the position of the breakpoint in the antenna propagation area,and based on the breakpoint segmentation,a large-scale statistical model for marshalling yards is established.

Extracting multi-objective multigraph features for the shortest path cost prediction:Statistics-based or learning-based?

Efficient airport airside ground movement(AAGM)is key to successful operations of urban air mobility.Recent studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate AAGM.Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs,however,previous work chiefly focused on single-objective simple graphs(SOSGs),treated cost enquires as search problems,and failed to keep a low level of computational time and storage complexity.This paper concentrates on the conceptual prototype MOMG,and investigates its node feature extraction,which lays the foundation for efficient prediction of shortest path costs.Two extraction methods are implemented and compared:a statistics-based method that summarises 22 node physical patterns from graph theory principles,and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space.The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction,while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs.Three regression models are applied to predict the shortest path costs to demonstrate the performance of each.Our experiments on randomly generated benchmark MOMGs show that(i)the statistics-based method underperforms on characterising small distance values due to severe overestimation;(ii)A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns;and(iii)the learning-based method consistently outperforms the statistics-based method,while maintaining a competitive level of computational complexity.

PREDICTIVE CONTROL FOR OPTIMAL PATH TERRAIN FOLLOWING SYSTEM

In this paper output predictive algorithm is applied to the design of predictive controller for an optimal path terrain following system. In this way, the error of path tracking is decreased to a minimum degree simply and efficiently and the computation time for the optimal path is shortened greatly. Therefore, the real-time processing of the optimal path terrain following system is made to be very helpful.

Simultaneous Measurement of Fringe Visibility and Path Predictability of Wave-Particle Duality

An experimental scheme to simultaneously obtain the information of fringe visibility and path predictability is designed. In a modified Young＇s double-slit experiment, two density filters rotating at different frequencies are placed before the two pineholes to encode path information. The spatial and temporal distributions of the output provide us with the wave and particle information of the single photons, respectively. The simultaneous measurement of the wave and particle information inevitably disturbs the system and thus causes some loss of the duality information, which is equal to the mixedness of the photonic state behind the density filters.

Quantum Path Predictability for an Electronic Mach-Zehnder Interferometer in Presence of Environment Induced Decoherence and Quantum Erasing Process

In this paper, we have estimated the temperature dependent path predictability for an electronic Mach-Zehnder interferometer. The increment of path predictability can directly be associated with stronger decoherence process. We have also theoretically predicted that placing two detectors in both the paths, which are at the same equilibrium temperature with the system, erases all the memory of path information and hence acts like a quantum eraser.

Prediction of network attack profit path based on NAPG model

The network attack profit graph(NAPG)model and the attack profit path predication algorithm are presented herein to cover the shortage of considerations in attacker’s subjective factors based on existing network attack path prediction methods.Firstly,the attack profit is introduced,with the attack profit matrix designed and the attack profit matrix generation algorithm given accordingly.Secondly,a path profit feasibility analysis algorithm is proposed to analyze the network feasibility of realizing profit of attack path.Finally,an opportunity profit path and an optimal profit path are introduced with the selection algorithm and the prediction algorithm designed for accurate prediction of the path.According to the experimental test,the network attack profit path predication algorithm is applicable for accurate prediction of the opportunity profit path and the optimal profit path.

A vision system based on CNN-LSTM for robotic citrus sorting Author links open overlay panel

Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and efficiency.In this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential data.The fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic grasping.High detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames later.The average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time performance.The results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.

An algorithm for trajectory prediction of flight plan based on relative motion between positions

Traditional methods for plan path prediction have low accuracy and stability. In this paper, we propose a novel approach for plan path prediction based on relative motion between positions(RMBP) by mining historical flight trajectories. A probability statistical model is introduced to model the stochastic factors during the whole flight process. The model object is the sequence of velocity vectors in the three-dimensional Earth space. First, we model the moving trend of aircraft including the speed(constant, acceleration, or deceleration), yaw(left, right, or straight), and pitch(climb, descent, or cruise) using a hidden Markov model(HMM) under the restrictions of aircraft performance parameters. Then, several Gaussian mixture models(GMMs) are used to describe the conditional distribution of each moving trend. Once the models are built, machine learning algorithms are applied to obtain the optimal parameters of the model from the historical training data. After completing the learning process, the velocity vector sequence of the flight is predicted by the proposed model under the Bayesian framework, so that we can use kinematic equations, depending on the moving patterns, to calculate the flight position at every radar acquisition cycle. To obtain higher prediction accuracy, a uniform interpolation method is used to correct the predicted position each second. Finally, a plan trajectory is concatenated by the predicted discrete points. Results of simulations with collected data demonstrate that this approach not only fulfils the goals of traditional methods, such as the prediction of fly-over time and altitude of waypoints along the planned route, but also can be used to plan a complete path for an aircraft with high accuracy. Experiments are conducted to demonstrate the superiority of this approach to some existing methods.