Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

A method of searching fault propagation paths in mechatronic systems based on MPPS model

In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propagation paths with their lengths if faults occur.First,the physical structure system,function behavior,and complex network theory are integrated to define a system structural-action network(SSAN).Second,based on the concept of SSAN,two properties of nodes and edges,i.e.,the topological property and reliability property,are combined to define the failure propagation property.Third,the proposed MPPS model provides all fault propagation paths and possible failure rates of nodes on these paths.Finally,numerical experiments have been implemented to show the accuracy and advancement compared with the methods of Function Space Iteration(FSI)and the algorithm of Ant Colony Optimization(ACO).

Implementation and comparative testing of turn-based algorithm for logit network loading

In order to evaluate the practicality and effectiveness of the turn-based algorithm for logit loading （TALL）, the TALL is implemented using C＋＋, and it is compared with a combination of the network-expanding method and the Dial algorithm based on the analysis of algorithm procedures. The TALL uses the arc-labeling shortest path searching, bidirectional star and the deque structure to directly assign the traffic flow, while the Dial algorithm should be used in an expanded network. The test results over realistic networks of eight cities show the superior performance of the TALL algorithm over the combination of the network-expanding method and the Dial algorithm, and the average processing time is reduced by 55. 4%. Furthermore, it is found that the operational efficiency of the TALL relates to the original densities of the cities. The average processing time is reduced by 65. 1% when the original density is about 14%, but the advantage of the TALL is not obvious with the increase in the original density.

Automatic wiring method for switchgear design

An automatic 3D wiring method for switchgear design is proposed in this paper. First, wiring constraints are created, and a corresponding evaluation model is proposed. Then, based on the structure of the cabinet, we propose a contour expansion scheme to construct rough paths. Different wiring features of the switchgear are used to connect rough local paths. All the paths are represented in a uniform data structure and forma path network. Finally, an improved A* algorithm is used to search the wiring path between the components in the routing network; the evaluation model is considered as heuristic rules for path searching. The result can satisfy the practical requirements of switchgear design. Experimental results are also provided.

Allocation of fixed transmission cost based on power flow tracing algorithm

In the electricity market, charging based on the traditional spot electricity price often results in the payment imbalance of electric network, and goes against the development of the power system. So, it is necessary to modify the spot price. The key of the modification lies in how to calculate the fixed unit transmission cost of each node, that is how to allocate the fixed transmission cost to users.To solve this problem, we develop a power flow tracing algrithm to modify the spot price. We put forward a path searching method based on the graph theory after studying the fundamental principle of power flow tracing and apply the method to the downstream tracing algorithm and upstream tracing algorithm according to the proportional distribution principle. Furthermore, to improve the computational efficiency of the algorithm, we introduce the branch expunction method to optimize the node order. By using the result of power flow tracing to get fixed node transmission cost and introducing it to modify the spot price, we obtain the synthetical price.The application to a 5-bus system prove the algorithm feasible.

Rectangle expansion A* pathfinding for grid maps

Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （REA＊）, is presented that improves the performance of A＊ significantly. REA＊ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles （instead of individual points within those boundaries） are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A＊. REA＊ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA＂ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA＊ can speed up a highly optimized A＊ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A＊.

Dispatching Fuel-cell Hybrid Electric Vehicles Toward Transportation and Energy Systems Integration

With the increasing integration of traditional elec-tric vehicles(EVs),the ensuing congestion and overloading issues have threatened the reliability of power grid operations.Hydrogen has been advocated as a promising energy carrier to achieve low-carbon transportation and energy(trans-energy)systems,which can support the popularization of fuel-cell hybrid EVs(FCHEVs)while enhancing the flexibility of power grids.In this paper,we propose an optimal scheduling framework for trans-energy systems that evaluates the merits of the hydrogen supply chain from water electrolysis,compressed storage and transportation to FCHEV utilization.A detailed FCHEV model is established,and mileage is modeled as a function of the stored electricity and hydrogen mass.A stochastic programming-based scheduling model is formulated,which minimizes the total cost of unit commitment and the hydrogen supply chain.The Dijkstra algorithm is adopted to search the shortest path for hydrogen transportation.Case studies demonstrate that FCHEVs can reduce the operational costs of tran-energy systems and facilitate the accommodation of renewable energy when compared to traditional EVs.Index Terms-Fuel-cell hybrid electric vehicle,hydrogen,mileage model,shortest path search,trans-energy systems.

Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

Network-Based Multiple UAVs Search Planning for Disaster Relief

This paper studies the use of multiple unmanned aerial vehicles(UAVs)for searching a victim of disaster.We propose a network-based optimization model for planning the search path of multiple UAVs in a disaster-stricken area and estimating the number of UAVs necessary for search.A heuristic algorithm is devised to solve the optimization model and applied to the problem instances taken from potential hazard areas in Japan.Our computational result shows relatively small number of UAVs is enough to cover the area in most of cases.We also give an investigation on the relation between search area and the number of UAVs necessary for search via regression methods.