Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.

Dynamic and electrical responses of a curved sandwich beam with glass reinforced laminate layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact

The dynamic responses and generated voltage in a curved sandwich beam with glass reinforced laminate(GRL)layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact(LVI)are investigated.The current study aims to carry out a dynamic analysis on the sandwich beam when the impactor hits the top face sheet with an initial velocity.For the layer analysis,the high-order shear deformation theory(HSDT)and Frostig's second model for the displacement fields of the core layer are used.The classical non-adhesive elastic contact theory and Hunter's principle are used to calculate the dynamic responses in terms of time.In order to validate the analytical method,the outcomes of the current investigation are compared with those gained by the experimental tests carried out by other researchers for a rectangular composite plate subject to the LVI.Finite element(FE)simulations are conducted by means of the ABAQUS software.The effects of the parameters such as foam modulus,layer material,fiber angle,impactor mass,and its velocity on the generated voltage are reviewed.

Research on Bond-Slip Constitutive Relation for Steel Reinforced Concrete Members

The constitutive relation of bond-slip on steel and concrete interface is proposed for short steel reinforced concrete （SRC） column. Based on the experimental research on bond-slip performance, a mechanical model of short SRC column in pulling or pushing test is established. By means of the elasto-plasticity theory the explicit formulation of bond-slip constitutive relation τ-s in different anchor-hold place of push and pull member is investigated under the conditions of balance and boundary. The study shows that the constitutive relation is relevant to the embedment length and the thickness of concrete cover. The results are continuous descriptions of bond-slip constitutive relation and can be used to analyze the non-linear performance of SRC members. Results indicate that the principle of the method is correct and it performs well for short SRC column.

The effect of a magnetic field on a 2D problem of fibre-reinforced thermoelasticity rotation under three theories

In the present paper, we introduce the coupled theory （CD）, Lord-Schulman （LS） theory, and Green-Lindsay （GL） theory to study the influences of a magnetic field and rotation on a two-dimensional problem of fibre-reinforced thermoelasticity. The material is a homogeneous isotropic elastic half-space. The method applied here is to use normal mode analysis to solve a thermal shock problem. Some particular cases are also discussed in the context of the problem. Deformation of a body depends on the nature of the force applied as well as the type of boundary conditions. Numerical results for the temperature, displacement, and thermal stress components are given and illustrated graphically in the absence and the presence of the magnetic field and rotation.

Preana: Game Theory Based Prediction with Reinforcement Learning

In this article, we have developed a game theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita’s algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players’ reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple steak-holders who have conflicting interests in economic, business, and political sciences. We have utilized game theory, expected utility theory, Median voter theory, probability distribution and reinforcement learning. We were able to reproduce Mesquita’s reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Irans 2013 presidential election to verify the accuracy of the prediction made by Preana.

Bending and stress analysis of polymeric composite plates reinforced with functionally graded graphene platelets based on sinusoidal shear-deformation plate theory

The bending and stress analysis of a functionally graded polymer composite plate reinforced with graphene platelets are studied in this paper.The governing equations are derived by using principle of virtual work for a plate which is rested on Pasternak’s foundation.Sinusoidal shear deformation theory is used to describe displacement field.Four different distribution patterns are employed in our analysis.The analytical solution is presented for a functionally graded plate to investigate the influence of important parameters.The numerical results are presented to show the deflection and stress results of the problem for four employed patterns in terms of geometric parameters such as number of layers,weight fraction and two parameters of Pasternak’s foundation.

Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Bond of Seawater Scoria Aggregate Concrete to Stainless Reinforcement

This study investigates the bond between seawater scoria aggregate concrete(SSAC)and stainless reinforcement(SR)through a series of pull-out tests.A total of 39 specimens,considering five experimental parameters—con-crete type(SSAC,ordinary concrete(OC)and seawater coral aggregate concrete(SCAC)),reinforcement type(SR,ordinary reinforcement(OR)),bond length(3,5 and 8 times bar diameter),concrete strength(C25 and C30)and concrete cover thickness(42 and 67 mm)—were prepared.The typical bond properties(failure pattern,bond strength,bond-slip curves and bond stress distribution,etc.)of seawater scoria aggregate concrete-stainless rein-forcement(SSAC-SR)specimen were systematically studied.Generally,the failure pattern changed with the con-crete type used,and the failure surface of SSAC specimen was different from that of OC specimen.SSAC enhanced the bond strength of specimen,while its effect on the deformation of SSAC-SR was negative.On aver-age,the peak slip of SSAC specimens was 20%lower while the bond strength was 6.7%higher compared to OC specimens under the similar conditions.The effects of variables on the bond strength of SSAC–SR in increasing order are concrete type,bond length,concrete strength and cover thickness.The bond-slip curve of SSAC-SR specimen consisted of micro-slipping,slipping and declining stages.It can be obtained that SSAC reduced the curve curvature of bond-slip,and the decline of curve became steep after adopting SR.The typical distribution of bond stress along bond length changed with the types of concrete and reinforcement used.Finally,a specific expression of the bond stress-slip curve considering the effects of various variables was established,which could provide a basis for the practical application of reinforced SSAC.

Failure Evaluation of Reinforced Concrete Beams Using Damage Mechanics and Classical Laminate Theory

The prediction of the behavior of reinforced concrete beams under bending is essential for the perfect design of these elements.Usually,the classical models do not incorporate the physical nonlinear behavior of concrete under tension and compression,which can underestimate the deformations in the structural element under short and long-term loads.In the present work,a variational formulation based on the Finite Element Method is presented to predict the flexural behavior of reinforced concrete beams.The physical nonlinearity due cracking of concrete is considered by utilization of damage concept in the definition of constitutive models,and the lamination theory it is used in discretization of section cross of beams.In the layered approach,the reinforced concrete element is formulated as a laminated composite that consists of thin layers,of concrete or steel that has been modeled as elastic-perfectly plastic material.The comparison of numerical load-displacement results with experimental results found in the literature demonstrates a good approximation of the model and validates the application of the damage model in the Classical Laminate Theory to predict mechanical failure of reinforced concrete beam.The results obtained by the numerical model indicated a variation in the stress-strain behavior of each beam,while for under-reinforced beams,the compressive stresses did not reach the peak stress but the stress-strain behavior was observed in the nonlinear regime at failure,for the other beams,the concrete had reached its ultimate strain,and the beam’s neutral axis was close to the centroid of the cross-section.

Social Engineering Attack-Defense Strategies Based on Reinforcement Learning

Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity,as human vulnerabilities are often the weakest link in the entire network.Such vulnerabilities are becoming increasingly susceptible to network security risks.Addressing the social engineering attack defense problem has been the focus of many studies.However,two main challenges hinder its successful resolution.Firstly,the vulnerabilities in social engineering attacks are unique due to multistage attacks,leading to incorrect social engineering defense strategies.Secondly,social engineering attacks are real-time,and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions.This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning(IQL)reinforcement learning method on human vulnerability attributes.The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy.Furthermore,the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks.The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning(QL)and deep Q-network(DQN)approaches in terms of time efficiency,taking 9.1%and 19.4%less time,respectively.Moreover,the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes.This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.

纤维增强复合材料加固混凝土桥梁结构研究进展

为总结纤维增强复合材料(FRP)在混凝土桥梁加固中的研究成果,拓宽FRP加固桥梁的思路,推动其在桥梁加固领域的广泛应用,阐述了FRP加固方法的发展及其特点,系统梳理了FRP加固方法在混凝土桥梁的研究进展。根据加固机理的不同,从抗弯加固、抗剪加固、抗压加固三方面加以归纳总结,介绍了几种常见的FRP加固方式、影响因素、破坏模式及其设计理论;分析了当前研究的不足,由此提出了一些可以继续深入研究的问题。结果表明:目前研究较多的有外贴FRP法、预应力FRP技术、表层嵌入法、FRP网格加固技术,其在不同的设计参数和使用条件下呈现出不同的破坏模式;外贴FRP片材容易过早发生剥离破坏,可以通过一些锚固措施提升界面黏结性,其中HB-FRP加固技术的锚固效果最好;预应力FRP加固技术因具有较高的材料强度利用率而展现出良好的应用前景;后续应深入研究FRP加固设计理论,综合考虑各参数对加固结构性能的影响,建立统一可靠且实用的计算模型;应进一步研究既有混凝土加固结构在多因素耦合作用下的长期性能和耐久性能,完善FRP加固混凝土桥梁设计方法和规范体系。

筋材布设方式对加筋土挡墙动力响应的影响

针对目前加筋土挡墙设计和施工中筋材布设方式大多为等长形的问题,提出一种倒梯形的筋材布设方式,并基于挡墙位移分区理论和有限差分Flac^(3D)数值模拟,建立加筋土挡墙三维分析模型,探讨不同峰值加速度下3种加筋土挡墙对位移、水平土压力、筋材拉应力及潜在破裂面的影响。结果表明,随峰值加速度增大,挡墙位移逐渐增大,同一荷载作用下,改变筋材布设方式,侧向水平位移减少9.3%,竖向沉降减少5.3%;3种形式挡墙水平土压力相差不大,最大水平土压力分布在挡墙的中下部;筋材拉应力随峰值加速度的增大,沿墙高从单峰型转化为双峰型分布,最大值位于挡墙中下部;潜在破裂面填土区破裂带的形状与筋材的布设方式有关。所提出的倒梯形筋材布设方式对加筋土挡墙的抗震效果更好,可为施工设计中加筋土挡墙筋材布设提供参考。

延伸斯金纳程序教学理论在病理生理学教学中的应用

斯金纳在程序性条件作用学说和强化理论基础之上创建了程序性教学法,该教学法是一种基于个性化学习的方法,有其独特的教学理论和原则。病理生理学是一门侧重于从代谢和功能变化角度研究疾病的病因、发病机制和转归的医学基础学科,其授课知识点之间逻辑性较强,不易理解。文章以病理生理学教学重点章节酸碱平衡紊乱为例,围绕学习目标要求,运用程序性教学法的理论和教学原则,设计酸碱平衡紊乱章节的程序性教学法的具体实施步骤,体现了程序性教学法的逻辑关联性强、自主性和层层递进式的学习特征,促进了学生对知识结构的梳理及对课程知识难点和重点的扎实掌握,探索了程序性教学法在病理生理学教学中应用的科学性和可操作性,为提高病理生理学教学效果提供可供参考的教学方法。

围缘扁钢加强的开圆孔无限平板应力解析算法

为了研究围缘扁钢加强的开圆孔无限平板应力解析计算方法,近似认为围缘扁钢区域的平均应力处于广义平面应力状态。运用复变函数论的方法,将应力函数表示为两个待定的解析函数;列出围缘扁钢的边界条件、围缘扁钢与平板之间的应力连接条件和位移连接条件,求出应力函数,根据求出的应力函数便可求出应力。为了描述围缘扁钢减缓孔口应力集中的效果,提出围缘扁钢加强系数的定义。算例计算表明:本文提出的解析算法计算结果与有限元法计算结果吻合良好,围缘扁钢加强的开圆孔无限平板应力普遍小于开圆孔无限平板应力,围缘扁钢加强效果明显;相同体积的加强构件,围缘扁钢的加强效果优于环形加厚板。

基于斯金纳强化理论的健康教育联合CICARE沟通对脊髓损伤患者间歇性导尿依从性、负性情绪及生活质量的影响

目的探讨基于斯金纳强化理论的健康教育联合CICARE沟通对脊髓损伤患者间歇性导尿依从性、负性情绪及生活质量的影响。方法采用目的抽样法,选取2021年12月至2022年11月在广西中医药大学附属瑞康医院康复医学科住院的85例脊髓损伤患者作为研究对象,按病区分为观察组43例和对照组42例,对照组接受常规护理,观察组在对照组的基础上给予基于斯金纳强化理论的健康教育联合CICARE沟通干预。在干预1个月后采用间歇性导尿依从性评估量表(ICAS)评估患者的导尿依从性;在干预前和干预1个月后分别采用神经源性膀胱症状评分表(NBSS)、焦虑自评量表(SAS)和抑郁自评量表(SDS)、WHO生活质量测定量表简表(WHOQOL-BREF)评估患者的神经源性膀胱症状、负性情绪(焦虑、抑郁症状)和生活质量。结果观察组患者干预1个月后的ICAS分级明显优于对照组,差异具有统计学意义(P<0.05);干预1个月后,两组患者的SAS评分和SDS评分均降低,且观察组患者SAS评分、SDS评分分别为(38.55±3.10)分、(39.83±3.59)分,明显低于对照组的(50.77±3.75)分、(51.67±4.92)分,差异均有统计学意义(P<0.05);干预1个月后,观察组患者的NBSS总分为(26.44±7.34)分,明显低于对照组的(34.71±12.56)分,差异有统计学意义(P<0.05);干预1个月后,观察组患者的WHOQOL-BREF总分为(51.12±5.89)分,明显高于对照组的(44.98±7.22)分,差异具有统计学意义(P<0.05)。结论基于斯金纳强化理论的健康教育联合CICARE沟通可提高脊髓损伤患者间歇性导尿依从性,改善其负性情绪和神经源性膀胱症状,提高生活质量。

基于组合权重的混凝土梁火灾后的安全评估

对火灾后建(构)筑物进行安全风险评估提供系统的评价体系,可为后期加固方案的制定提供参考依据。文章从混凝土梁的材料参数、作用参数和几何参数等方面选取混凝土强度、混凝土表面颜色、最高温度、受火时间、混凝土裂缝和混凝土脱落6个评价指标,基于组合权重及模糊理论建立了火灾后混凝土梁安全风险评价模型,应用于火灾后某厂房的安全风险评价,并对比分析了规范评价结果。结果表明:运用所建模型评定的损伤等级与规范评定的损伤等级吻合度较高,验证了所建立评价体系的准确性和合理性。

无人机集群对抗决策算法研究综述

无人机集群博弈对抗已经成为未来战争的发展趋势,无人机对抗决策算法的选择对提升无人机集群作战能力至关重要。本文深入探讨了基于规则的、基于博弈论的和基于神经网络的三大类无人机集群博弈对抗决策算法,并对它们的优势和局限性进行了全面分析与总结。在此基础上,提出将“基于多智能体强化学习的信用分配模型”和“基于角色的多智能体强化学习模型”应用于无人机集群博弈对抗的研究思路。最后,强调了选择适当的决策算法对于提高无人机集群作战效能的重要性,并为未来无人机对抗决策的发展提出了有益的建议,为相关领域的研究和应用提供了深入见解。

基于博弈论的入侵检测与响应优化综述

当前网络规模急剧增加,各类入侵过程也逐渐向复杂化、多样化的趋势发展。网络攻击带来的损失越来越严重,针对各类安全事件的检测发现以及查处响应也变得日益困难。为了快速识别各类网络安全事件并做出相应的响应,入侵检测与响应技术变得越来越重要。入侵检测系统(IDS)能否识别复杂的攻击模式以及分析大量的网络流量主要取决于其精度和配置,这使得入侵检测与响应的优化问题成为网络与系统安全的重要需求,并且成为一个活跃的研究主题。现有的研究成果已经提出了很多可以优化入侵检测和响应效率的方法,其中,将博弈论应用在入侵检测与响应的研究日益增多。博弈论提供了一种框架去捕获攻击者和防御者的交互,采用了一种定量的方法评估系统的安全性。本文在分析了入侵检测与响应系统和博弈论的基本原理的基础上,介绍了当前基于博弈论的入侵检测与响应优化问题的现有解决方案,并且讨论了这些解决方案的局限性以及给出了未来的研究方向。首先,详细介绍了入侵检测与博弈论的背景知识,回顾了常用的入侵检测系统基本原理,评估方法,常用的数据集以及经典的安全领域中的博弈论模型。其次,按照基于博弈论的入侵检测与响应优化问题的类型进行了分类介绍,根据攻击的先后顺序对网络安全架构优化、IDS配置与效率优化、IDS的自动化响应优化以及分布式入侵检测架构优化等技术的研究现状进行归纳、分析、总结,并分析了现有方案的优缺点,进而分析可能的解决方案。然后针对将博弈论应用于入侵检测与响应中面临的挑战进行了分析与讨论。最后展望了未来的研究方向以及发展趋势。

碳纳米管增强复合材料Timoshenko梁弯曲和屈曲行为分析

考虑碳纳米管(carbon nanotubes,CNTs)的尺度效应,研究宏观尺度下碳纳米管增强复合材料(carbon nanotubes reinforced composites,CNTRCs)梁的弯曲和屈曲行为。在EMT(Eshelby-Mori-Tanaka)方法的基础上,利用非局部理论提出了可表征CNTs尺度效应的非局部EMT本构模型。根据Timoshenko梁理论,采用哈密顿原理得到CNTRCs梁的静力学微分方程和边界条件。求解简支边界条件下CNTRCs梁的弯曲响应和极限屈曲载荷,并与文献进行对比验证所建模型和求解方法的正确性。分析了CNTs的尺度效应参数和体积分数以及复合材料梁的长细比等因素对简支CNTRCs梁弯曲响应和极限屈曲载荷的影响规律。结果表明,考虑CNTs的尺度效应会削弱结构等效刚度,且CNTs体积分数和尺度效应参数对大长细比CNTRCs梁的弯曲响应和极限屈曲载荷的影响幅度较大。