Quickly obtaining densely dispersed coherent particles in steel matrix and its related mechanical property

Densely distributed coherent nanoparticles(DCN)in steel matrix can enhance the work-hardening ability and ductility of steel simultaneously.All the routes to this end can be generally classified into the liquid-solid route and the solid-solid route.However,the formation of DCN structures in steel requires long processes and complex steps.So far,obtaining steel with coherent particle enhancement in a short time remains a bottleneck,and some necessary steps remain unavoidable.Here,we show a high-efficiency liquid-phase refining process reinforced by a dynamic magnetic field.Ti-Y-Mn-O particles had an average size of around(3.53±1.21)nm and can be obtained in just around 180 s.These small nanoparticles were coherent with the matrix,implying no accumulated dislocations between the particles and the steel matrix.Our findings have a potential application for improving material machining capacity,creep resistance,and radiation resistance.

Determination of liquid viscosity based on dual-frequency-bandparticle tracking

An optical-tweezers-based dual-frequency-band particle tracking system was designed and fabricated for liquid viscositydetection. On the basis of the liquid viscosity dependent model of the particle’s restricted Brownian motion with theFax´en correction taken into account, the liquid viscosity and optical trap stiffness were determined by fitting the theoreticalprediction with the measured power spectral densities of the particle’s displacement and velocity that were derived from thedual-frequency-band particle tracking data. When the SiO2 beads were employed as probe particles in the measurements ofdifferent kinds of liquids, the measurement results exhibit a good agreement with the reported results, as well as a detectionuncertainty better than 4.6%. This kind of noninvasive economical technique can be applied in diverse environments forboth in situ and ex situ viscosity detection of liquids.

Hydrodynamic characteristics and particle tracking of 90° lateral intakes at an inclined river slope

Lateral intakes are common in rivers.The pump effciency and sediment deposition are determined by the local hydrodynamic characteristics and mainstream division width.The hydraulic characteristics of lateral withdrawal from inclined river slopes at different intake elevations should be investigated.Meanwhile,the division width exhibits significant vertical non-uniformity at an inclined river slope,which should be clarified.Hence,a three-dimensional(3-D)hydrodynamic and particle-tracking model was developed with the Open Source Field Operation and Manipulation(Open FOAM),and the model was validated with physical model tests for 90°lateral withdrawal from an inclined side bank.The flow fields,withdrawal sources,and division widths were investigated with different intake bottom elevations,withdrawal discharges,and main channel velocities.This study showed that under inclined side bank conditions,water entered the intake at an oblique angle,causing significant 3-D spiral flows in the intake rather than two-dimensional closed recirculation.A lower withdrawal discharge,a lower bottom elevation of the intake,or a higher main channel velocity could further strengthen this phenomenon.The average division width and turbulent kinetic energy were smaller under inclined side bank conditions than under vertical bank conditions.With a low intake bottom elevation,a low withdrawal discharge,or a high main channel velocity,the sources of lateral withdrawal were in similar ranges near the local inclined bank in the vertical direction.Under inclined slope conditions,sediment deposition near the intake entrance could be reduced,compared to that under vertical slope conditions.The results provide hydrodynamic and sediment references for engineering designs for natural rivers with inclined terrains.

Numerical Modelling of Sediment Particle Transportation on a Navigation Inlet Using the Particle Tracking Model (PTM)

This paper investigates particle transportation using a numerical model application approach to understand the final fate of suspended sediment particle masses due to a dredging operation in a navigational harbor inlet using PTM (Particle Tracking Model). The investigation applied PTM and simulated particle transportation at a navigational harbor called St Jerome Creek Inlet in Chesapeake Bay in Maryland. The United States Army Corps of Engineers (USACE), Maryland District, designed jetties for the inlet, which, when constructed, would minimize dredging requirements from once in a two-year period to once in a ten-year period. In the meantime, due to the frequent dredging requirements of the inlet, there exists a need to understand the fate of the suspended sediments from the dredging operations to assess the environmental impact on the aquatic environment and the coastal community. This study used PTM to simulate the transportation of sediments in a 30-day period during a dredging operation. Ten sediment source locations were selected as possible sites from which dredged materials could be introduced into the flow system. The model output was analyzed to draw conclusions. Results showed that most suspended sediment particle masses moved from their initial site locations and settled along the shoreline, whilst the sediments that found their way out of the inlet system towards the ocean migrated southward and settled approximately 6 miles at the tip of the mainland. The objective of the study is to track sediment particles from a dredging operation. This would be significant in tracking possible contaminants in an aquatic environment for future environmental management decisions.

A Distributed Particle Filter Applied in Single Object Tracking

Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.

Counting of alpha particle tracks on imaging plate based on a convolutional neural network

Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.

Numerical simulation of particle tracks in the cold gas dynamic spraying process

In this study,the distribution behavior of the particle flow field in cold gas dynamic spraying （CGDS） was simulated through the Computational fluid dynamics （CFD） method. Traces of the particles with different diameters in the gas flow field were analyzed, and effects of fiat and sphere substrates on the particle tracks were also compared .Simulation results indicate that different escaping directions of particles flow with the two substrates. These investigations. gave instructions on how to design the powder recovery and dusting machines in a CGDS system.

Electric-Jet Assisted Layer-by-Layer Deposition of Gold Nanoparticles to Prepare Conducting Tracks

A suspension of 15nm diameter gold nanoparti-cles has been deposited along a line on a silicon substrate with the assistance of a jet generated in an electric field. In order to control the evaporation of the solvent used to suspend the gold nanoparticles, a heating device was used to change the substrate temperature. Layer-by- layer deposition enabled the direct writing of gold tracks having an electrical resistivity of 1.8 × 10-7 Ωm, only about an order of magnitude above the electrical resistivity of bulk gold.

Face tracking algorithm based on particle filter with mean shift importance sampling

The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

Bayesian target tracking based on particle filter

For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter （EKF） proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo （MCMC） method, the resampling step, ere novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.

A correlative classifiers approach based on particle filter and sample set for tracking occluded target

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.

Improved Particle Filter for Target Tracking

A new improved particle filter algorithm with the simplified UT （unscented transformation） and the modified unscented Kalman filter （UKF） proposal distribution is presented. The scaling factor is added to adaptively estimate on line and to improve the filtering performance. An adaptive algorithm is developed. In the bearings-only tracking experiments, the results confirm the improved particle filter algorithm outperforms others.

A New Particle Filter for Nonlinear Tracking Problems

A new particle filter is presented for nonlinear tracking problems. Inpractice, maneuvering target-tracking systems are usually nonlinear and incompletely observed, andthe main difficulty of maneuvering target-tracking problem lies in the fact that the maneuverabilityat every step is of high uncertainties. Here a new smoothing particle filter algorithm is proposed,which combines the particle filter to tackle the non-linear and non-Gaussian peculiarities of theproblem, together with smoothing of the PDF of system modes and thus settles the estimate problem ofthe target maneuverability. The simulation comparison with the auxiliary particle filters showsthat the approach has superiority and yields performance improvements in solving nonlinear trackingproblems.

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

Target tracking in glint noise using a MCMC particle filter

In radar target tracking application, the observation noise is usually non-Gaussian, which is also referred as glint noise. The performances of conventional trackers degra de severely in the presence of glint noise. An improved particle filter, Markov chain Monte Carlo particle filter (MCMC-PF), is applied to cope with radar target tracking when the measurements are perturbed by glint noise. Tracking performance of the filter is demonstrated in the present of glint noise by computer simulation.

Particle agglomeration and inhibition method in the fluidized pyrolysis reaction of waste resin

This work investigated the pyrolysis reaction of waste resin in a fluidized bed reactor.It was found that the pyrolysis-generated ash would adhere to the surface of ceramic particles,causing particle agglomeration and defluidization.Adding kaolin could effectively inhibit the particle agglomeration during the fluidized pyrolysis reaction through physical isolation and chemical reaction.On the one hand,kaolin could form a coating layer on the surface of ceramic particles to prevent the adhesion of organic ash generated by the pyrolysis of resin.On the other hand,when a sufficient amount of kaolin(-0.2%(mass))was added,the activated kaolin could fully contact with the Na+ ions generated by the pyrolysis of resin and react to form a high-melting aluminosilicate mineral(nepheline),which could reduce the formation of low-melting-point sodium sulfate and thereby avoid the agglomeration of ceramic particles.

Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

Particle filter initialization in non-linear non-Gaussian radar target tracking

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.