Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

Novel cued search strategy based on information gain for phased array radar

A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.

Elastic Full Waveform Inversion Based on the Trust Region Strategy

In this paper, we investigate the elastic wave full-waveform inversion (FWI) based on the trust region method. The FWI is an optimization problem of minimizing the misfit between the observed data and simulated data. Usually, the line search method is used to update the model parameters iteratively. The line search method generates a search direction first and then finds a suitable step length along the direction. In the trust region method, it defines a trial step length within a certain neighborhood of the current iterate point and then solves a trust region subproblem. The theoretical methods for the trust region FWI with the Newton type method are described. The algorithms for the truncated Newton method with the line search strategy and for the Gauss-Newton method with the trust region strategy are presented. Numerical computations of FWI for the Marmousi model by the L-BFGS method, the Gauss-Newton method and the truncated Newton method are completed. The comparisons between the line search strategy and the trust region strategy are given and show that the trust region method is more efficient than the line search method and both the Gauss-Newton and truncated Newton methods are more accurate than the L-BFGS method.

Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

Effects of electroacupuncture on the expression of p70 ribosomal protein S6 kinase and ribosomal protein S6 in the hippocampus of rats with vascular dementia

This study investigated the mechanism underlying electroacupuncture therapy for vascular dementia through electroacupuncture at the acupoints of Baihui （DU20）, Dazhui （DU14）, and bilateral Shenshu （BL23） in a rat model of vascular dementia produced by bilateral middle cerebral artery occlusion. Morris water maze test showed that electroacupuncture improved the learning ability of vascular dementia rats. Western blot assay revealed that the expression of p70 ribosomal protein S6 kinase and ribosomal protein S6 in vascular dementia rats was significantly increased after electroacupuncture, compared with the model group that was not treated with acupuncture. The average escape latency was also shortened after electroacupuncture, and escape strategies in the spatial probe test improved from edge and random searches, to linear and trending swim pathways. The experimental findings indicate that electroacupuncture improves learning and memory ability by up-regulating expression of p70 ribosomal protein S6 kinase and ribosomal protein S6 in the hippocampus of vascular dementia rats.

Smoke root detection from video sequences based on multi-feature fusion

Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.

Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

A HYBRID METHOD FOR SOLVING VARIATIONAL INEQUALITY PROBLEMS

By using Fukushima's differentiable merit function,Taji,Fukushima and Ibaraki have given a globally convergent modified Newton method for the strongly monotone variational inequality problem and proved their method to be quadratically convergent under certain assumptions in 1993.In this paper a hybrid method for the variational inequality problem under the assumptions that the mapping F is continuously differentiable and its Jacobian matrix Δ F(x) is positive definite for all x∈S rather than strongly monotone and that the set S is nonempty,polyhedral,closed and convex is proposed.Armijo type line search and trust region strategies as well as Fukushima's differentiable merit function are incorporated into the method.It is then shown that the method is well defined and globally convergent and that,under the same assumptions as those of Taji et al.,the method reduces to the basic Newton method and hence the rate of convergence is quadratic.Computational experiences show the efficiency of the proposed method.

Toward Robust and Efficient Low-Light Image Enhancement:Progressive Attentive Retinex Architecture Search

In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.

Solving Service Selection Problem Based on a Novel Multi-Objective Artificial Bees Colony Algorithm

Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial(NP)-hard. This paper proposes a novel multi-objective artificial bees colony(n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service(QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial(Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization(NBACO)algorithm and co-evolution algorithm.

DESIGN OF A SOFTWARE AGENT FOR BUSINESS GAMING SIMULATION

This paper introduces a software agent as a virtual player of the business game called Bakery Game. The business game is a tool to understand business and management principles through experience in a virtual world. In order to construct software agents for the gaming simulation, which is able to participate in a game and become human players' worthy rival, we combine a simple model that considers the property of Bakery Game with a strategy adjustment model. The agent has so-called strategy parameters that are updated through the experience. The agent changes his strategy depending on particular situation. The agent participates in the game that is regenerated from log data in various situations.

Robust visual tracking via randomly projected instance learning

Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.

Backscattering wave mode analysis of an elastic tube and parameter reconstruction

In this paper, we first discuss the direct scattering problem for a cylindrical homogeneous clastic tube with a geometrical method, and then study the inverse problem. The heuristics of B-F procedure (Best-First search strategy) is used. The inner radius, outer radius, sound velocitics of the tube and sound velocity of the internal medium are obtained with this heuristics.

An enhanced cascading failure model integrating data mining technique

An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.

NONMONOTONE LOCAL MINIMAX METHODS FOR FINDING MULTIPLE SADDLE POINTS

In this paper,by designing a normalized nonmonotone search strategy with the BarzilaiBorwein-type step-size,a novel local minimax method(LMM),which is a globally convergent iterative method,is proposed and analyzed to find multiple(unstable)saddle points of nonconvex functionals in Hilbert spaces.Compared to traditional LMMs with monotone search strategies,this approach,which does not require strict decrease of the objective functional value at each iterative step,is observed to converge faster with less computations.Firstly,based on a normalized iterative scheme coupled with a local peak selection that pulls the iterative point back onto the solution submanifold,by generalizing the Zhang-Hager(ZH)search strategy in the optimization theory to the LMM framework,a kind of normalized ZH-type nonmonotone step-size search strategy is introduced,and then a novel nonmonotone LMM is constructed.Its feasibility and global convergence results are rigorously carried out under the relaxation of the monotonicity for the functional at the iterative sequences.Secondly,in order to speed up the convergence of the nonmonotone LMM,a globally convergent Barzilai-Borwein-type LMM(GBBLMM)is presented by explicitly constructing the Barzilai-Borwein-type step-size as a trial step-size of the normalized ZH-type nonmonotone step-size search strategy in each iteration.Finally,the GBBLMM algorithm is implemented to find multiple unstable solutions of two classes of semilinear elliptic boundary value problems with variational structures:one is the semilinear elliptic equations with the homogeneous Dirichlet boundary condition and another is the linear elliptic equations with semilinear Neumann boundary conditions.Extensive numerical results indicate that our approach is very effective and speeds up the LMMs significantly.