High-speed penetration of ogive-nose projectiles into thick concrete targets:Tests and a projectile nose evolution model

The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.

Battlefield target intelligence system architecture modeling and system optimization

To address the current problems of poor generality,low real-time,and imperfect information transmission of the battlefield target intelligence system,this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare.First,an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling,which reduces the difficulty of the planning and design process.The method introduces the Department of Defense architecture framework(DoDAF)modeling method,the multi-living agent(MLA)theory modeling method,and other combinations for planning and modeling.A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed.Further,the liveness analysis of the battlefield target intelligence system is carried out,and the problems of the existing system are presented from several aspects.And the technical prediction of the development and construction is given,which provides directional ideas for the subsequent research and development of the battlefield target intelligence system.In the end,the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets(CPN)simulation software.The analysis demonstrates the reasonable integrity of its logic.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

An Effective Multiple Model Least Squares Method in Tracking of a Maneuvering Target

A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Targeting Effect Study of ￣(3) H-Mitoxantrone Nanosphereson Hepatocellular Carcinoma(HCC) Model in Nude Mice

The distribution of ￣(3)H-mitoxantrone polybutyl cyanoacrylate nanospheres(￣(3)H-DHAQ-PBCA-NS)in the viscera,muscle and tumors of human hepatocellular carcinoma (HCC)model in nude mice was studied with liquid scintillation counting techniique. The results showed that the ￣(3)H-DHAQ-PBCA-NS had remarkable liver targeting effect. The content of ￣(3)H-DHAQ-PBCA-NSin liver and heterotopic liver tumor was found to be 71.31±10. 49% of total amount of drug in animal body. It was also found that the content of ￣(3)H-DHAQ-PBCA-NS in liver was higher than that in liver tissue, and the content of ￣(3)H-DHAQ-PBCA-NS in annpit tumor was higher than that in armpit muscle tissue,but had no significant difference;It provides an ideal preparation for the DHAQ admini-stration.

A Study on the Multi-Compartment Linear Circulation Pharmacokinetic Model for the Targeting Drug Delivery System

By analyzing the observed phenomena and the data collected in the study, a multi-compartment linear circulation model for targeting drug delivery system was developed and the function formulas of the drug concentration-time in blood and target organ by computing were figured out. The drug concentration-time curve for target organ can be plotted with reference to the data of drug concentration in blood according to the model. The pharmacokinetic parameters of the drug in target organ could also be obtained. The practicability of the model was further checked by the curves of drug concentration-time in blood and target organ(liver) of liver-targeting nanoparticles in animal tests. Based on the liver drug concentration-time curves calculated by the function formula of the drug in target organ, the pharmacokinetic behavior of the drug in target organ(liver) was analyzed by statistical moment, and its pharmacokinetic parameters in liver were obtained. It is suggested that the (relative targeting index( can be used for quantitative evaluation of the targeting drug delivery systems.

Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

Establishing a rat model of spastic cerebral palsy by targeted ethanol injection

Spastic cerebral palsy is generally considered to result from cerebral cortical or pyramidal tract damage. Here, we precisely targeted the left pyramidal tract of 2-month-old Sprague-Dawley rats placed on a stereotaxic instrument under intraperitoneal anesthesia. Based on the rat brain stereotaxic map, a 1-mm hole was made 10 mm posterior to bregma and 0.8 mm left of sagittal suture. A microsyringe was inserted perpendicularly to the surface of the brain to a depth of 9.7 mm, and 15 wL of ethanol was slowly injected to establish a rat model of spastic cerebral palsy. After modeling, the rats appeared to have necrotic voids in the pyramidal tract and exhibited typical signs and symptoms of flexion spasms that lasted for a long period of time. These findings indicate that this is an effective and easy method of establishing a rat model of spastic cerebral palsy with good reproducibility. Ethanol as a chemical ablation agent specifically and thoroughly damages the py- ramidal tract, and therefore, the animals display flexion spasms, which are a typical symptom of the disease.

MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination.Focal demyelination is associated with relapsing-remitting multiple sclerosis,while progressive forms of the disease show axonal degeneration and neuronal loss.The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity.MicroRNAs(miRNAs)are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases.A review of recent studies with the experimental autoimmune encephalomyelitis animal model(mostly female mice 6–12 weeks of age)has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset(asymptomatic)stage when assessed in blood plasma and urine exosomes,and spinal cord tissue.The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes,brain and spinal cord tissue,and at the post-peak(chronic)stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue.Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease.Interestingly,experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a,miR-23b,miR-497,miR-26a,and miR-20b,or by suppression of miR-182,miR-181c,miR-223,miR-155,and miR-873.Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course.Additionally,studies should be performed with male mice of a similar age,and with aged male and female mice.

Moving target detection based on improved ghost suppression and adaptive visual background extraction

Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation,the detection results in many false detections for the highly dynamic background.To solve these problems,an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper.Firstly,with the pixel’s temporal and spatial information,the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence.Secondly,the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background,to acquire the adaptive segmentation threshold.Thirdly,the update rate is adjusted based on the complexity of the background.Finally,the detected result goes through a post-processing to achieve better detection results.The experimental results show that the improved algorithm will not only quickly suppress the“ghost”,but also have a better detection in a complex dynamic background.

A New RCS Statistical Model of Radar Target

In this paper, the drawbacks of conventional target fluctuation models used in radar target modeling are set out. It is usually difficult to statistically model a real target because there are very few parameters which can be used to approximate the probability density function (PDF) of a real target's radar cross section (RCS) in conventional target models. A new method of statistical modeling is suggested, according to which the first nth central moment of real target's RCS, combined with the Legendre orthogonal polynomials, is used to reconstruct the PDF of the target's RCS. The relationship between the coefficients of the Legendre polynomials and the central moments of RCS are deduced mathematically. Through a practical computing example, the error-of-fit is shown as a function of the orders of Legendre coefficients. By comparing the errors-of-fit caused by both the new model and the conventional models, it is concluded that the new nonparametric method for statistical modeling of radar targets is superior.

MicroRNAs in mouse and rat models of experimental epilepsy and potential therapeutic targets

Epilepsy is a common and serious neurological disease that causes recurrent seizures. The brain damage caused by seizures can lead to depression, anxiety, cognitive impairment, or disability. In almost all cases chronic seizures are difficult to cure. MicroRNAs are widely expressed in the central nervous system and play important roles in the pathogenesis of several neurological disorders, including epilepsy. A variety of animals(mostly mice and rats) have been used to induce experimental epilepsy using different protocols and miRNA profiling performed. Most of the recent studies reviewed had performed miRNA profiling in hippocampal tissues and a large number of microRNAs were dysregulated when compared to controls. Most notably, miR-132-3p,-146a-5p,-10a-5p,-21a-3p,-27a-3p,-142a-5p,-212-3p,-431-5p, and-155 were upregulated in both the mouse and rat studies. Overexpression of miR-137 and miR-219 decreased seizure severity in a mouse epileptic model, and suppression of miR-451,-10a-5p,-21a-5p,-27a-5p,-142a-5p,-431-5p,-155, and-134 had a positive influence on seizure behavior. In the rat studies, overexpression of miR-139-5p decreased neuronal damage in drug-resistant rats and inhibition of miR-129-2-3p,-27a-3p,-155,-134,-181a, and-146a had a positive effect on seizure behavior and/or reduced the loss of neuronal cells. Further studies are warranted using adult female and immature male and female animals. It would also be helpful to test the ability of specific agomirs and antagomirs to control seizure activity in a subhuman primate model of epilepsy such as adult marmosets injected intraperitoneally with pilocarpine or cynomolgus monkeys given intrahippocampal injections of kainic acid.

A numerical model study on multi-species harmful algal blooms coupled with background ecological fields

Based on systematized physical, chemical, and biological modules, a multi-species harmful algal bloom （HAB） model coupled with background ecological fields was established. This model schematically embod-ied that HAB causative algal species and the background ecological system, quantified as total biomass, were significantly different in terms of the chemical and biological processes during a HAB while the inter-action between the two was present. The model also included a competition and interaction mechanism between the HAB algal species or populations. The Droop equation was optimized by considering tempera-ture, salinity, and suspended material impact factors in the parameterization of algal growth rate with the nutrient threshold. Two HAB processes in the springs of 2004 and 2005 were simulated using this model. Both simulation results showed consistent trends with corresponding HAB processes observed in the East China Sea, which indicated the rationality of the model. This study made certain progress in modeling HABs, which has great application potential for HAB diagnosis, prediction, and prevention.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Bidirectional Background Modeling for Video Surveillance

Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed method to combine color and texture characteristics. Suppression and relaxation are the two key strategies to resist illumination changes and shadow disturbance. The proposed method is quite efficient and is capable of resisting illumination changes. Experimental results show that our method is suitable for real-word scenes and real-time applications.

Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.