Maritime Target Saliency Detection for UAV Based on the Stimulation Competition Selection Mechanism of Raptor Vision

A maritime target saliency detection method inspired by the stimulation competition and selection mechanism of raptor vision is presented for the airborne vision system of unmanned aerial vehicle(UAV)in an unknown maritime environment.The stimulation competition and selection mechanism in the visual pathway of raptor vision based on the phenomenon of raptor capturing prey in complex scenes are studied.Then,the mathematical model of the stimulation competition and selection mechanism of raptor vision is established and employed for the salient object detection.Popular image datasets and practical scene datasets are applied to verify the effectiveness of the presented method.Results show that the detection performance of the proposed method is better than that of other comparison methods.The proposed algorithm provides an idea for maritime target salient detection and cross-domain joint mission for UAV or other unmanned equipment.

Establishment of Knowledge Base Management System and the Type Selection of Mechanism for Mechanism Kinematic Scheme

Firstly data standardization technology and combined classification method have been applied to carry out classification of kinematic behaviors and mechanisms in the mapping field between the kinematic behavior level and the mechanism level of conceptual design.The principle of computer coding and storing have been built to give a fast and broad selection of mechanisms that meets the requirements of basic motion characters.Then on the basis of mentioned above,the heuristic matching propagation principle (HMPP) of kinematic behaviors and its true table serves as a guide to perform mechanism types selection.Finally an application is given to indicate its practicability and effectiveness.

Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

For the past few decades,the internet of underwater things(IoUT)otained a lot of attention in mobile aquatic applications such as oceanography,diver network monitoring,unmanned underwater exploration,underwater surveillance,location tracking system,etc.Most of the IoUT applications rely on acoustic medium.The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate,attenuation,limited bandwidth,limited battery,limited memory,connectivity problem,etc.One of the significant applications of IoUT include monitoring underwater diver networks.In order to perform a reliable and energy-efficient communication system in the underwater diver networks,a smart underwater hybrid softwaredefined modem(UHSDM)for the mobile ad-hoc network was developed that is used for selecting the best channel/medium among acoustic,visible light communication(VLC),and infrared(IR)based on the criteria established within the system.However,due to the mobility of underwater divers,the developed UHSDMmeets the challenges such as connectivity errors,frequent link failure,transmission delay caused by re-routing,etc.During emergency,the divers are most at the risk of survival.To deal with diver mobility,connectivity,energy efficiency,and reducing the latency in ADN,a handover mechanism based on pre-built UHSDM is proposed in this paper.This paper focuses on(1)design of UHSDM for ADN(2)propose the channel selection mechanism in UHSDM for selecting the best medium for handover and(3)propose handover protocol inADN.The implementation result shows that the proposed mechanism can be used to find the new route for divers in advance and the latency can be reduced significantly.Additionally,this paper shows the real field experiment of air tests and underwater tests with various distances.This research will contribute much to the profit of researchers in underwater diver networks and underwater networks,for improving the quality of services(QoS)of underwater applications.

The Kinematics Analysis of Heald Selecting Mechanism of Rotary Electronic Dobby

In order to improve the reliability of the mechanical movement of the rotary electronic dobby, the kinematics analysis of the heald selection mechanism is carried out and the simulation is carried out with Matlab. Firstly, the operation mechanism of the heald selection mechanism is analyzed in detail. The conjugate cam is mapped. The cam profile curve is fitted with cubic spline interpolation. Secondly, based on the overall analysis method and the complex vector method, the kinematics analysis of the key components after the high pair low generation is performed, and the angular displacement and angular velocity of each component are calculated with the rotation of the active cam. Finally, the movement curve diagram is drawn with Matlab, which lays the foundation for the dynamic analysis and in-depth study of the selection mechanism in the future.

An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

Effective constrained optimization algorithms have been proposed for engineering problems recently.It is common to consider constraint violation and optimization algorithm as two separate parts.In this study,a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems.Based on the improved pbest selection method,an adaptive differential evolution approach is proposed,which helps the population jump out of the infeasible region.If all the individuals are infeasible,the top 5%of infeasible individuals are selected.In addition,a modified truncatedε-level method is proposed to avoid trapping in infeasible regions.The proposed adaptive differential evolution approach with an improvedεconstraint processmechanism(IεJADE)is examined on CEC 2006 and CEC 2010 constrained benchmark function series.Besides,a standard IEEE-30 bus test system is studied on the efficiency of the IεJADE.The numerical analysis verifies the IεJADE algorithm is effective in comparisonwith other effective algorithms.

Auditory attention model based on Chirplet for cross-corpus speech emotion recognition

To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.

Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

An Improved DV-Hop Localization Algorithm Based on Selected Anchors

Wireless Sensor Network(WSN)based applications has been extraordinarily helpful in monitoring interested area.Only information of surrounding environment with meaningful geometric information is useful.How to design the localization algorithm that can effectively extract unknown node position has been a challenge in WSN.Among all localization technologies,the Distance Vector-Hop(DV-Hop)algorithm has been most popular because it simply utilizes the hop counts as connectivity measurements.This paper proposes an improved DV-Hop based algorithm,a centroid DV-hop localization with selected anchors and inverse distance weighting schemes(SIC-DV-Hop).We adopt an inverse distance weighting method for average distance amelioration to improve accuracy.Also in this paper,we propose an inclusive checking rule to select proper anchors to avoid the inconsistency existing in centroid localization schemes.Finally,an improved multilateration centroid method is presented for the localization.Simulations are conducted on two different network topologies and experiments results show that compared with existing DV-Hop based algorithms,our algorithm can significantly improve the performance meanwhile cost less network resource.

A MACHINE LEARNING APPROACH FOR MECHANISM SELECTION IN COMPLEX NEGOTIATIONS

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

Observation of selective surface element substitution in FeTe_(0.5)Se_(0.5) superconductor thin film exposed to ambient air by synchrotron radiation spectroscopy

A systematic investigation of oxidation on a superconductive Fe Te_（0.5）Se_（0.5）thin film,which was grown on Nb-doped SrTiO_3（001） by pulsed laser deposition,has been carried out.The sample was exposed to ambient air for one month for oxidation.Macroscopically,the exposed specimen lost its superconductivity due to oxidation.The specimen was subjected to in situ synchrotron radiation photoelectron spectroscopy（PES） and x-ray absorption spectroscopy（XAS） measurements following cycles of annealing and argon ion etching treatments to unravel what happened in the electronic structure and composition after exposure to air.By the spectroscopic measurements,we found that the as-grown FeTe_（0.5）Se_（0.5）superconductive thin film experienced an element selective substitution reaction.The oxidation preferentially proceeds through pumping out the Te and forming Fe–O bonds by O substitution of Te.In addition,our results certify that in situ vacuum annealing and low-energy argon ion etching methods combined with spectroscopy are suitable for depth element and valence analysis of layered structure superconductor materials.

Selective leaching of calcium from mechanically activated mixed rare earth concentrate

The loss of rare earths(REs)takes place during the pre-decalcification process of mixed rare earth concentrate.In an effort to reduce such RE loss,a novel idea to improve the leaching selectivity of Ca to REs by applying selective mechanical activation was proposed.First,regarding the key minerals affecting the leaching selectivity of Ca to REs,the differences in the mechanical activation behaviors of CaF_(2) and REFCO_(3) were studied,and we find that the lattice strain of CaF_(2) increases from 0.21%to 0.42%,whereas that of REFCO_(3) increases from 0.31%to 0.40%.Notably,CaF_(2) demonstrates a larger lattice strain than REFCO_(3),indicating greater mechanical activation energy storage and higher leaching activity.Next,the HCl leaching process was studied.A significant leaching selectivity of Ca to REs,from 21.6 to 35.1,is achieved through mechanical activation.The Ca leaching rate reaches 80.7%when the RE loss is 2.3%in the activated sample.This study provides an novel approach for achieving selective extraction of specific components via mechanical activation pretreatment.

Effect of Powder Particle Shape on the Properties of In Situ Ti–TiB Composite Materials Produced by Selective Laser Melting

This work studied the preparation of starting powder mixture influenced by milling time and its effect on the particle morphology （especially the shape） and, consequently, density and compression properties of in situ Ti-TiB composite materials produced by selective laser melting （SLM） technology. Starting powder composite system was prepared by mixing 95 wt% commercially pure titanium （CP-Ti） and 5 wt% titanium diboride （TiB2） powders and subsequently milled for two different times （i.e. 2 h and 4 h）. The milled powder mixtures after 2 h and 4 h show nearly spherical and irregular shape, respectively. Subsequently, the resultant Ti-5 wt% TiB2 powder mixtures were used for SLM processing. Scanning electron microscopy image of the SLM-processed Ti-TiB composite samples show needle-shape TiB phase distributed across the Ti matrix, which is the product of an in-situ chemical reaction between Ti and TiB2 during SLM. The Ti-TiB composite samples prepared from 2 h and 4 h milled Ti-TiB2 powders show different relative densities of 99.5% and 95.1%, respectively. Also, the compression properties such as ultimate strength and compression strain for the 99.5% dense composite samples is 1421 MPa and 17.8%, respectively, which are superior to those （883 MPa and 5.5%, respectively） for the 95.1% dense sample. The results indicate that once Ti and TiB2 powders are connected firmly to each other and powder mixture of nearly spherical shape is obtained, there is no additional benefit in increasing the milling time and, instead, it has a negative effect on the density （i.e. increasing porosity level） of the Ti-TiB composite materials and their mechanical properties.

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.