Research on localization algorithm of naval vessel for submarine based on bearing-range measurements

Combined with naval vessel practical antisubmarine equipment of towed linear array sonar,a mathematical model of naval vessel localization for submarine based on bearing measurement was built,and localization algorithm was given to solve submarine movement parameters.Localizaiton errors were analyzed.Based on localization model and algorithm,simulations were done to study the effect of factors such as initial distance between submarine and the naval vessel,submarine initial bearing angle measured by the naval vessel and submarine course on localization performance,and then simulation results were given and analyzed.The results have practical value to instruct real antisubmarine.Simulation results show that different target movement situations have great influence on sonar detection and localization performance,so the reasonable choice of sonar position and detection bearing according to the target movement situation can improve sonar detection and localization performance to some degree.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Distributed sound source localization algorithm with sound velocity calibration in windy environments

A new sound source localization method with sound speed compensation is proposed to reduce the wind influence on the performance of conventional TDOA （Time Difference of Arrival） algorithms. First, the sound speed is described as a set of functions of the unknown source location, to approximate the acoustic velocity field distribution in the wind field. Then, they are introduced into the TDOA algorithm, to construct nonlinear equations. Finally, the particle swarm optimization algorithm is used to estimate the source location. The simulation results show that the proposed algorithm can significantly improve the localization accuracy for different wind velocities, source locations and test area sizes. The experimental results show that the proposed method can reduce localization errors to about 40% of the original error in a four nodes localization system.

A Localization Algorithm in Wireless Sensor Network Based on Positioning Group Quality

Localization is fundamental component for many critical applicationsin wireless sensor networks (WSNs). However, DV-Hop localization algorithmand its improved ones cannot meet the requirement of positioning accuracy fortheir high localization errors. This paper proposes a localization algorithm basedon positioning group quality (LA-PGQ). The average estimate hop size was firstcorrected by link singularity and difference between the estimation hop lengthand true hop length among beacons, the best positioning group was constitutedfor unknown node by using node trust function and positioning group qualityevaluation function to choose three beacons with best topological distribution.Third, LA-PGQ algorithm uses two-dimensional hyperbolic algorithm instead ofthe classical three-side method/least square method to determine the coordinates ofnodes, which are more accurate. Simulation results show the positioning accuracyof LA-PGQ algorithm is obviously improved in WSNs, and the average localizationerror of LA-PGQ algorithm is remarkable lower than those of the DV-Hopalgorithm and its improved algorithm and Amorphous, under both the isotropyand anisotropy distributions.

Localization Algorithm of Wireless Sensor Network Based on Concentric Circle Distance Calculation

Node Localization is one of the key technology in the field of wireless sensor network(WSN)that has become a challenging research topic under the lack of distance measurement.In order to solve this problem,a localization algorithm based on concentric circle distance calculation(LACCDC)is proposed.The LA-CCDC takes the beacon as the center of the concentric circle,then divides the task area into concentric circles with the k communication radius of sensor,which forms concentric rings.The node located in the k hops ring intersects the concentric circle with(k−1)r radius that forms an intersection area.This area is used to calculate the distance from the beacon to the unknown node,hyperbola is then adopted to locate the unknown node.In the application scenario with node random distribution,the simulation results show that the LA-CCDC algorithm gets the node location with low error under different node number,different beacons and different communication radius of sensor.

LOCAL STRUCTURE-PRESERVING ALGORITHMS FOR THE KLEIN-GORDON-ZAKHAROV EQUATION

In this paper, using the concatenating method, a series of local structure-preserving algorithms are obtained for the Klein-Gordon-Zakharov equation, including four multisymplectic algorithms, four local energy-preserving algorithms, four local momentumpreserving algorithms;of these, local energy-preserving and momentum-preserving algorithms have not been studied before. The local structure-preserving algorithms mentioned above are more widely used than the global structure-preserving algorithms, since local preservation algorithms can be preserved in any time and space domains, which overcomes the defect that global preservation algorithms are limited to boundary conditions. In particular, under appropriate boundary conditions, local preservation laws are global preservation laws.Numerical experiments conducted can support the theoretical analysis well.

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter （EKF） is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation （MLE） and traditional KF algorithm.

Research on a bifurcation location algorithm of a drainage tube based on 3D medical images

Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

A Multiple-Neighborhood-Based Parallel Composite Local Search Algorithm for Timetable Problem

This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.

Local and parallel finite element algorithms for time-dependent convection-diffusion equations

Local and parallel finite element algorithms based on two-grid discretization for the time-dependent convection-diffusion equations are presented. These algorithms are motivated by the observation that, for a solution to the convection-diffusion problem, low frequency components can be approximated well by a relatively coarse grid and high frequency components can be computed on a fine grid by some local and parallel proce- dures. Hence, these local and parallel algorithms only involve one small original problem on the coarse mesh and some correction problems on the local fine grid. One technical tool for the analysis is the local a priori estimates that are also obtained. Some numerical examples are given to support our theoretical analvsis.

Transitionless driving on local adiabatic quantum search algorithm

We apply the transitionless driving on the local adiabatic quantum search algorithm to speed up the adiabatic process. By studying quantum dynamics of the adiabatic search algorithm with the equivalent two-level system, we derive the transi- tionless driving Hamiltonian for the local adiabatic quantum search algorithm. We found that when adding a transitionless quantum driving term Ht~ （t） on the local adiabatic quantum search algorithm, the success rate is 1 exactly with arbitrary evolution time by solving the time-dependent Schr6dinger equation in eigen-picture. Moreover, we show the reason for the drastic decrease of the evolution time is that the driving Hamiltonian increases the lowest eigenvalues to a maximum of

A Systematic Overview of Underwater Wireless Sensor Networks:Applications,Challenge and Research Perspectives

Underwater Wireless Sensor Networks(UWSNs)are becoming increasingly popular in marine applications due to advances in wireless and microelectronics technology.However,UWSNs present challenges in processing,energy,and memory storage due to the use of acoustic waves for communication,which results in long delays,significant power consumption,limited bandwidth,and packet loss.This paper provides a comprehensive review of the latest advancements in UWSNs,including essential services,common platforms,critical elements,and components such as localization algorithms,communication,synchronization,security,mobility,and applications.Despite significant progress,reliable and flexible solutions are needed to meet the evolving requirements of UWSNs.The purpose of this paper is to provide a framework for future research in the field of UWSNs by examining recent advancements,establishing a standard platform and service criteria,using a taxonomy to determine critical elements,and emphasizing important unresolved issues.

CONVERGENCE ANALYSIS OF SOME FINITE ELEMENT PARALLEL ALGORITHMS FOR THE STATIONARY INCOMPRESSIBLE MHD EQUATIONS

By combination of iteration methods with the partition of unity method(PUM),some finite element parallel algorithms for the stationary incompressible magnetohydrodynamics(MHD)with different physical parameters are presented and analyzed.These algorithms are highly efficient.At first,a global solution is obtained on a coarse grid for all approaches by one of the iteration methods.By parallelized residual schemes,local corrected solutions are calculated on finer meshes with overlapping sub-domains.The subdomains can be achieved flexibly by a class of PUM.The proposed algorithm is proved to be uniformly stable and convergent.Finally,one numerical example is presented to confirm the theoretical findings.

Detection of Link Fault Using Risk Modelling and Improving QOS Parameters in IP/WDM Networks

An operational backbone network is connected with many routers and other devices. Identifying faults in the network is very difficult, so a fault localization mechanism is necessary to identify fault and alleviate it and correct the faults in order to reduce the network performance degradation. A risk model needs to be devised based on the dynamic database by creating alternate path and the network is reconfigured by identifying dynamic paths. In this paper, an on-demand link state routing approach is used for handling failures in IP backbone networks and a localization algorithm is used to improve QOS parameters based on threshold value of gateway. It is proved that on-demand link state routing guarantees loop-free forwarding to reachable destinations regardless of the number of failures in the network, and in case of localization algorithm using modification process packet loss is avoided based on threshold value of gateway. Heuristic algorithm is also used for reconfiguration of dynamic path for effective fault localization. In this paper, in order to change the traffic condition, reconfiguration strategic is dynamically used. Dijikstra’s shortest path algorithm has been used to determine the shortest path between node pairs. Using risk modeling mechanism, a small set of candidate faults is identified. The concept of Fault Localization is used to minimize the fault occurring in the node and sends original path to node pairs. The localization algorithm based on MODIFICATION PROCESS, packet loss is avoided in the network by checking threshold value of gateway. If the threshold value is maximum, router directly forwards the packet to destination through gateway and if the threshold value is minimum, router compresses the packet and forwards the packet to destination with notification via gateway.

Optimization of multi-model ensemble forecasting of typhoon waves

Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models （model-ensembles）. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting.

A novel algorithm for automatic localization of human eyes

Based on geometrical facial features and image segmentation, we present a novel algorithm for automatic localization of human eyes in grayscale or color still images with complex background. Firstly, a determination criterion of eye location is established by the prior knowledge of geometrical facial features. Secondly, a range of threshold values that would separate eye blocks from others in a segmented face image (i.e., a binary image) are estimated. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, once two eye blocks appear from the segmented image, they will be detected by the determination criterion of eye location. Finally, the 2D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. To avoid the background interference, skin color segmentation can be applied in order to enhance the accuracy of eye detection. The experimental results demonstrate the high efficiency of the algorithm and correct localization rate.

Parallel finite element computation of incompressible magnetohydrodynamics based on three iterations

Based on local algorithms,some parallel finite element(FE)iterative methods for stationary incompressible magnetohydrodynamics(MHD)are presented.These approaches are on account of two-grid skill include two major phases:find the FE solution by solving the nonlinear system on a globally coarse mesh to seize the low frequency component of the solution,and then locally solve linearized residual subproblems by one of three iterations(Stokes-type,Newton,and Oseen-type)on subdomains with fine grid in parallel to approximate the high frequency component.Optimal error estimates with regard to two mesh sizes and iterative steps of the proposed algorithms are given.Some numerical examples are implemented to verify the algorithm.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

LOCAL AND PARALLEL FINITE ELEMENT ALGORITHMS FOR THE NAVIER-STOKES PROBLEM

Based on two-grid discretizations, in this paper, some new local and parallel finite element algorithms are proposed and analyzed for the stationary incompressible Navier- Stokes problem. These algorithms are motivated by the observation that for a solution to the Navier-Stokes problem, low frequency components can be approximated well by a relatively coarse grid and high frequency components can be computed on a fine grid by some local and parallel procedure. One major technical tool for the analysis is some local a priori error estimates that are also obtained in this paper for the finite element solutions on general shape-regular grids.