Comparison of the severity of injury of hippocampal neuron in rats induced by simulated push-pull maneuver at various degrees

BACKGROUND： Push-pull effect is often caused during maneuver, and the changes of unconsciousness induced can affect or damage cerebral neurons at various degrees. OBJECTIVE： To observe the effect of simulated push-pull maneuver at various degrees on injury of hippocampal neurons in rats and analyze its phase effect. DESIGN： Randomized control study.SETTING ： Physiological Department of Jilin Medical College.MATERIALS： A total of 40 healthy male Wistar rats, of clean grade, weighting 205-300 g, aged 3-4 months, were randomly divided into control group （n=4） and three push-pull experimental groups, including ＋2 Gz group （intensity： -2 Gz to ＋2 Gz, n=12）, ＋6 Gz group （-6 Gz to ＋6 Gz, n=12） and ＋8 Gz group （-8 Gz to ＋8 Gz, n=12）.METHODS： The experiment was completed in the Physiological Department of Jilin Military Medical College from March 2002 to May 2003. ① Rats in the experimental groups were put at the specially rolling arm of animal centrifugal machine. Then, they were pushed and pulled with ±2 Gz, ±6 Gz and ±8 Gz, respectively. The jolt was 1 Gz/s. However, rats in control group were not treated with any ways. ② Stroke index and neurological evaluation were performed on rats in the experimental groups at 0.5, 6 and 24 hours after push-pull. Stroke index was 25 points in total. The higher the scores were, the severer the cerebral injury was. Neurological evaluation was 10 points in total. The higher the scores were, the severer the nerve injury was. ③ Hippocampal tissue in brain of rats were selected to cut into sections at each time points, and form and distribution of neurons were observed in hippocampal areas with HE staining. Degrees of neuronal injury in hippocampal CA1 area were assayed after push-pull at various degrees with electron microscope. ④ Measurement data were compared with t test.MAIN OUTCOME MEASURES：① Stroke index and neurological evaluation; ② form and distribution of neurons in hippocampal areas;③ degrees of neuronal injury in hippocampal CA1 area.RESULTS： A total of 40 rats were involved in the final analysis. ① Stroke index and neurological evaluation of rats in experimental groups： At 30 minutes and 6 hours after push-pull exposure, stroke index and neurological evaluation were higher in ±6Gz group and ±8 Gz group than those in control group （P 〈 0.01）, especially at 6 hours after push-pull exposure, those in ±8 Gz group were the highest at each time points [（11.00±2.16）, （5.75±1.70） points]. At 24 hours after exposure, those were decreased as compared with those within the former two time points, but the values were still higher than those in control group （P 〈 0.05-0.01）. ② Results of HE staining： At 6 and 24 hours after exposure, partially neuronal degeneration was observed in pyramidal layer in ±6 Gz group and ±8 Gz group, including crenation of neurons, tdangle or polygon, and karyopycnosis, especially the injury in ±8 Gz group was the most obvious at 6 hours after exposure. ③ Results of ultrastructure with electron microscope： Partially neuronal degeneration at various degrees was observed in hippocampal CA1 area in ±2 Gz group at 6 hours after exposure and in ±6 Gz group and ±8 Gz group at 6 and 24 hours after exposure. At 6 hours after exposure, nucleus of hippocampal neurons in ±8 Gz group was irregular and umbilication. Caryotin was aggregated, nuclear matrix was swelled and disorder, and vacuolation was also observed. Rough endoplasmic reticulum was expanded, mitochondrium was swelled, and crista was disappeared.CONCLUSION： ① Push-pull cannot damage hippocampal neurons of rats in ±2 Gz group. ② Exposure can cause injury of hippocampal neurons of rats in ±6Gz group and ±8 Gz group, especially the injury is the severest at 6 hours after exposure in ±8 Gz group and relieves gradually 24 hours later.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Cooperative guidance law based on super-twisting observer for target maneuvering

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets,a guidance law with temporal consistency constraint based on the super-twisting observer is proposed.Firstly,the relative motion equations between multiple missiles and targets are established,and the topological model among multiple agents is considered.Secondly,based on the temporal consistency constraint,a cooperative guidance law for simultaneous arrival with finite-time convergence is derived.Finally,the unknown target maneuver-ing is regarded as bounded interference.Based on the second-order sliding mode theory,a super-twisting sliding mode observer is devised to observe and track the bounded interfer-ence,and the stability of the observer is proved.Compared with the existing research,this approach only needs to obtain the sliding mode variable which simplifies the design process.The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect.It ensures successful cooperative attacks,even when confronted with strong maneuvering targets.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

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.

Helicopter Maneuver Trajectory Tracking Control Based on Implicit Model and LADRC

To enhance the stability of helicopter maneuvers during task execution,a composite trajectory tracking controller design based on the implicit model(IM)and linear active disturbance rejection control(LADRC)is proposed.Initially,aerodynamic models of the main and tail rotor are created using the blade element theory and the uniform inflow assumption.Subsequently,a comprehensive flight dynamic model of the helicopter is established through fitting aerodynamic force fitting.Subsequently,for precise helicopter maneuvering,including the spiral,spiral up,and Ranversman maneuver,a regular trim is undertaken,followed by minor perturbation linearization at the trim point.Utilizing the linearized model,controllers are created for the IM attitude inner loop and LADRC position outer loop of the helicopter.Ultimately,a comparison is made between the maneuver trajectory tracking results of the IM‑LADRC and the conventional proportional-integral-derivative(PID)control method is performed.Experimental results demonstrate that utilizing the post-trim minor perturbation linearized model in combination with the IM‑LADRC method can achieve higher precision in tracking results,thus enhancing the accuracy of helicopter maneuver execution.

Aerodynamic, Kinematic and Control Coupling Simulation of Aircraft Maneuvering Flight

Aiming at the high angle of attack pull-up and multi-channel roll pull-up coupling problems of high maneuvering aircraft, this paper establishes the flight attitude control rate by means of unsteady flow numerical solution, dynamic unstructured nested mesh assembly method and numerical solution method of flight mechanics equation. On this basis, a virtual flight simulation platform integrating pneumatics, motion and control is established. Based on this virtual flight simulation platform, F-16 aircraft is simulated by high angle of attack pull-up flight mode and multi-channel roll pull-up coupling flight mode. Finally, the influence of rudder on the yaw control channel is investigated. The results show that the numerical virtual flight simulation platform established in this paper has the ability to simulate maneuvering flight of aircraft.

Trajectory and Correction Maneuver During the Transfer from Earth to Halo Orbit

This article addresses the design of the trajectory transferring from Earth to Halo orbit, and proposes a timing closed-loop strategy of correction maneuver during the transfer in the frame of circular restricted three body problem （CR3BP）. The relation between the Floquet multipliers and the magnitudes of Halo orbit is established, so that the suitable magnitude for the aerospace mission is chosen in terms of the stability of Halo orbit. The stable manifold is investigated from the Poincar6 mapping defined which is different from the previous researches, and six types of single-impulse transfer trajectories are attained from the geometry of the invariant manifolds. Based on one of the trajectories of indirect transfer which are ignored in the most of literatures, the stochastic control theory for imperfect information of the discrete linear stochastic system is applied to design the trajectory correction maneuver. The statistical dispersion analysis is performed by Monte-Carlo simulation,

两种新的push-pull平衡的大数据量无线传感器网络数据分发算法

无线传感器网络中如何获得较低的通信代价同时在事件数据的push和pull之间实现更好的平衡是各种数据分发算法共同追求的目标.分析了目前已公认较好的两种典型的有结构和无结构的数据分发算法,指出了它们的优缺点.在此基础上,结合这两种算法使用的push-pull策略,针对不同应用环境下的无线传感器网络的ALL型查询的特定需求,提出了两种基于有结构和无结构存储模式相结合的混合型数据分发算法,分别是Hybrid-Dcs-Cn1(HDC1)算法和Hybrid-Dcs-Cn2(HDC2)算法.分析表明这两种算法在保证push-pull之间平衡的前提下解决了已有算法存在的热点问题:存储拷贝数多和查询性能低,能更好地适应传感器网络的特点,是两种能量高效的数据分发算法.

Fish maneuver analysis system based on sequence images

The aim is to establish an automatic system to analyze the maneuver performance of fish. A high speed camera （1 000 frame/s） is employed to record fast-start maneuver. Three steps are taken to analyze the kinematics： first, the midline in the first image is partitioned into equal interval lengths and the coordinates of all inter segmental points are saved. Secondly, these points coordinates are searched in the next frame with the digital image correlation （DIC） method, then these points are fitted with a spline curve function. Repeat this step until all the midlines are figured out frame by frame. Finally, according to the variety of midlines, the kinematics of the fast-start is calculated. Using this system to test carp C-start, the duration is divided into two stages： stage 1 is defined as the formation of the C shape and stage 2 as the return flip of the tail followed with forward motion. By tracing the middle line, the kinematic parameters of turning rate, centre of mass （CM） turning rate, CM turning radius, etc. are obtained.

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.

Push-Pull型pFLASH开关单元结构设计及特性

基于90 nm eFLASH工艺设计并制备了一种新型抗辐照Push-Pull型pFLASH开关单元,并对其特性进行了研究。该结构由2个2T-FLASH管（T1/T2）和1个信号传输PMOS管（T3）组成,采用带带隧穿（BTBT）编程方式和福勒-诺德海姆（FN）擦除方式实现其＂开/关＂态功能。同时,对其＂开/关＂态特性进行表征,研究其耐久性和电荷保持特性,最后,对其抗总剂量（TID）能力进行评估。实验结果表明：该器件的＂T1编程-T2擦除＂与＂T1擦除-T2编程＂态均可以实现信号传输管的＂开/关＂态功能,其阈值窗口的均值约为10.5 V;在工作电压为-1.2 V条件下,T3管的＂开＂态驱动电流均值约为0.92 mA,＂关＂态漏电流低于40 pA,且均表现出了良好的一致性。同时,该器件的循环擦/写次数可达10 000次,在25℃的＂开/关＂态应力条件下寿命大于10年,抗总剂量能力可达150 krad（Si）以上。

An approach to improving maneuver performance of coning algorithm

Aiming to improve the maneuver performance of the strapdown inertial navigation attitude coning algorithm a new coning correction structure is constructed by adding a sample to the traditional compressed coning correction structure. According to the given definition of classical coning motion the residual coning correction error based on the new coning correction structure is derived. On the basis of the new structure the frequency Taylor series method is used for designing a coning correction structure coefficient and then a new coning algorithm is obtained.Two types of error models are defined for the coning algorithm performance evaluation under coning environments and maneuver environments respectively.Simulation results indicate that the maneuver accuracy of the new 4-sample coning algorithm is almost double that of the traditional compressed 4-sample coning algorithm. The new coning algorithm has an improved maneuver performance while maintaining coning performance compared to the traditional compressed coning algorithm.

基于第三方的Push-Pull逆向物流模式

首先阐述了逆向物流的内涵、功能以及基于第三方逆向物流的提出,其次分析了外包逆向物流的优势,最后分析了生产控制系统中的Push-Pull方法.在此基础上,提出基于第三方的Push-Pull逆向物流模式.

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.

基于DTN网络的PUSH-PULL策略研究

DTN网络是新一代的网络体系结构,PUSH-PULL是解决DTN网络中拥塞控制的一种有效策略,介绍了DTN网络中拥塞控制的研究现状,并对PUSH-PULL的各个策略进行了阐述,通过NS2搭建DTN网络环境并通过模拟对PUSH-PULL的7种策略进行仿真,模拟仿真结果表明,采用PUSH-PULL策略的可以有效的提高信息的传输率.

高并发数据共享系统基于Push-Pull混合模式的性能优化

随着面向服务架构的大规模分布式系统的应用,多个系统之间由于数据集成的需要,彼此间要高并发传递大量数据,特别是状态数据的同步,对数据的实时性要求越来越高。数据单纯通过客户端以Pull(拉取)模式获取已经不能满足实时性要求,高频率服务调用也会给服务端数据库带来较大的压力。单纯通过服务端以Push(推送)模式推送给客户端也不能满足客户端对个性化数据的需求,大量推送既会给服务端带来较大压力,又会造成客户端数据处理不及时。本文针对高并发数据共享系统应用过程中产生的性能问题,提出一种基于pub/sub消息处理的Push-Pull混合模式优化方案,实践表明该方案极大地提高了系统可用性及数据安全性,对同类系统的性能优化具有较好的借鉴作用。

Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

To reach a higher level of autonomy for unmanned combat aerial vehicle(UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system,the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result,adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty,to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.