Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats

We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Numerical optimisation of a classical stochastic system for targeted energy transfer

The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mass is subjected to a zero-mean Gaussian white noise excitation,the main objective of this study is to maximise the efficiency of the targeted energy transfer in the system.A surrogate optimisation algorithm is proposed for this purpose and adopted for the stochastic framework.The optimisations are conducted separately for the nonlinear stiffness coefficient alone as well as for both the nonlinear stiffness and damping coefficients together.Three different optimisation cost functions,based on either energy of the system’s components or the dissipated energy,are considered.The results demonstrate some clear trends in values of the nonlinear energy sink coefficients and show the effect of different cost functions on the optimal values of the nonlinear system’s coefficients.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

社会责任视角下的纺织印染工艺优化与实践

随着全球经济的快速发展,纺织印染行业作为资源密集型行业,面临着巨大的社会责任挑战。在环境污染、能源消耗、资源管理以及劳动力和社会福祉方面,纺织印染行业亟需寻找一种符合可持续发展原则,实现经济效益的优化途径。在社会责任视角下,优化纺织印染工艺,既要求技术创新,也要求在生产全过程中实施社会责任。这种纺织印染工艺的优化与实践可以为纺织印染行业提供一条实现环境可持续和社会责任兼顾的优化路径。

非冷链商品配送路径优化研究--以京东配送为例

如何有效降低商品配送成本是物流企业的关注重点,学界已就带时间窗的商品配送路径优化算法展开了相关研究,但相关算法还存在着过早陷入局部最优或无法收敛的问题。由此提出一种改进的变邻域遗传搜索算法(VNS-GA),以非冷链商品配送为研究对象,构造起求解物流配送车辆路径规划的数学模型。首先,以配送成本和缺货惩罚成本的最小化作为实现目标,构建了包括车辆使用成本、配送运输成本和时间窗口惩罚成本的配送路径优化模型;其次,运用变邻域遗传优化算法来实现多目标物流配送路径的优化;最后,以京东某北京配送中心的物流配送为例,运用MATLAB软件对VNS-GA算法模型的科学性及有效性进行仿真验证。经实证,VNS-GA算法与传统算法相比具有更好的全局和局部搜索能力。研究期望为配送车辆调度与配送路径规划提供更优的路径选择模型,从而降低物流配送成本和减少便利店因缺货造成的损失。

基于模糊分析的船用柴油机燃用生物柴油性能优化

为优化生物柴油/柴油船用发动机的综合性能,利用AVL-Fire软件构建混合燃料发动机燃烧室模型,将仿真值与台架试验值进行对比验证其准确性,并通过燃用不同比例的生物柴油确立其最佳掺混比;最后,采用正交试验设计安排5个发动机运行参数进行多参数优化匹配,将NO排放量和指示功率作为评价指标,权重分别设置为0.6和0.4,对试验结果进行模糊数学分析。结果表明:对发动机性能影响大小顺序为EGR率(a_(1))、进气压力(a_(2))、进气温度(a_(3))、喷油提前角(a_(4))、喷油孔直径(a_(5));最优参数组合为:生物柴油掺混比30%、a_(1)=12.5%、a_(2)=0.173MPa、a_(3)=315.15K、a_(4)=18.6°CA、a_(5)=0.32mm,该组指示功率为52.7kW与原机55kW相比略低,NO排放量为5.37×10^(-5)%相比于原机8.5×10^(4)%降低了94.7%。该方法可以在保证发动机动力性的前提下,大幅降低NO排放量。

轻量化高速列车制动盘材料-结构研究进展

400 km/h高速列车紧急制动的强热负荷,导致制动盘温升高、温度分布梯度大和热衰减强,已超出钢质制动盘能载极限,开发耐高温强耐磨的制动盘材料,同时提升制动盘通风散热能力成为制动技术发展瓶颈。综述高速制动盘材料从高强度铸钢和锻钢、轻量化铝基复合材料至耐高温耐磨碳陶复合材料和表面改性技术发展历程,随着碳陶复合材料的台架试验和表面改性技术的摩擦磨损试验研究的深化,表面改性轻质复合材料是制动盘材料未来发展方向。概述通风制动盘内部散热层散热筋结构形貌优化、摩擦层表面耐磨性能改善的发展现状,内部散热筋由直通道设计逐渐演化为弯曲、支柱以及分形通道,镂空结构能更好地加速空气流动提高散热;表面摩擦层引入打孔或划线,更易于摩擦粉尘的抛离从而稳定摩擦因数,保持制动效果;但复杂通风导流结构和打孔划线增加了制备工艺的难度,提出高速制动盘结构构型设计需要综合材料热力学性能、制备工艺及耐热耐磨需求,形成材料-结构-功能—体化设计理念,为实现更高速的制动盘优化设计提供参考。

少数民族大学生中华民族共同体意识培育路径探析

新的历史时代,以民族团结为主题的各民族共同发展已经成为中国式现代化建设的重要内容。加强少数民族大学生中华民族共同体意识建设,就要以辩证唯物主义和历史唯物主义的马克思主义科学理论为指导思想,以习近平总书记关于加强和改进民族工作重要思想的立场、观点和方法为培育抓手,发挥各方合力,共同促进少数民族大学生的中华民族共同体意识教育。具体应着力抓好以下几方面的工作:一要加大少数民族大学生马克思主义民族团结理论教育;二要促进少数民族大学生对习近平新时代中国特色社会主义思想关于民族论述的正确理解;三要加强对少数民族大学生社会主义核心价值体系的培育;四要落实“石榴红”项目建设,培养少数民族大学生干部队伍。最终,使少数民族学生真正成为拥护中国共产党领导、认同社会主义核心价值观的有为之士。

结合精英初始化和K近邻的蛇优化算法

蛇优化算法(SO)是一种受自然界中蛇生存行为启发产生的元启发式优化算法。原始蛇优化算法存在收敛速度慢、易陷入局部最优的问题,因此提出了一种结合精英初始化和K近邻的改进蛇优化算法(elite initia-lization and K-nearest neighbors improved snake optimizer,EKISO)。首先,为了提高初始种群质量,在种群初始化阶段提出精英初始化的方法,根据种群精英个体产生优质初始种群个体;其次,通过振荡因子优化螺旋觅食策略扩大全局勘探阶段的搜索范围、提高算法的局部逃逸能力;最后,在局部开发阶段提出K近邻思想的位置更新方法,增强种群个体之间的信息交互能力,从而加快收敛速度、提高收敛精度。利用14个经典测试函数和4个CEC2017测试函数将该方法与其他7种优化算法进行对比,证明EKISO收敛速度更快、精度更高且不易陷入局部最优。为了进一步验证EKISO的实用性与可行性,将EKISO应用于压力容器设计问题中,通过实验对比分析可知,EKISO在处理实际优化问题上具有一定的优越性。

人工智能时代生物信息学学科发展和人才培养模式研究

生物信息学作为生命科学与生物技术/信息技术研究领域的关键交叉学科,对生物经济和数字经济的贡献日益显著。当前,生物信息学仍面临学科体系不健全、定位模糊以及交叉合作不充分等挑战,多模态高维度生物大数据的准确性、分析处理和共享整合问题也考验着生物信息学的发展。在建设科技强国的过程中,生物信息学是生物经济产业布局的关键环节。与此同时,人工智能技术的融入正引发生命科学研究范式的转变,促使生物信息学从认知科学向工程创造的STEM并存模式方向发展。此外,生物信息学面临人才培养同质化和优秀青年人才“内卷”的困境,需要构建多层次培养体系和优化科研环境,培养具有战略眼光的科学家。由此,应加强顶层设计,完善学科体系与教学体系;建立多元化人才培养体系;全面推进“101计划”;优化教育资源分配和教学模式创新。

冻融循环下玄武岩纤维混凝土冲击力学性能预测模型

冻融循环下纤维混凝土的劣化规律是寒区服役混凝土工程安全性和耐久性评价的重要依据,现有服役混凝土工程的安全性和耐久性评价的研究具有工作量大、成本高、周期长等特点,构建基于机器学习的高精度力学性能预测模型已成为本领域研究热点。为探究冻融循环后玄武岩纤维混凝土冲击力学性能的高精度预测模型,采用SHPB装置对冻融循环后BFRC开展动态冲击压缩力学性能试验,并构建机器学习-Optuna混合预测模型,对60组以玄武岩纤维体积掺量、冻融循环次数、动荷载冲击速度为影响因素建立的动态峰值应力样本数据集进行预测。结果表明:k近邻、Lasso、多层感知机、极度梯度提升树和随机森林5种经典机器学习模型的预测准确度均较高,说明机器学习算法对于冻融循环后BFRC动态力学性能预测具有良好的预测效果,其中随机森林算法为最优预测算法;RF-Optuna混合预测模型显示出0.9754的拟合优度,具有良好的预测精度;非数据集工况预测表明,该混合模型对于各影响因素均具有良好泛化能力。研究成果可为冻融循环条件下BFRC动态力学性能的快捷精准预测提供参考。

基于文献计量的建筑碳排放评估进展与分析

基于Citespace软件,利用文献计量法,对2011年1月到2022年2月期间的建筑碳排放评估的关键成果以及关键词共现分析和共被引分析的结果进行研究,分析碳排放评估的研究热点,结果得出未来的研究重点应关注建筑碳排放评估优化以及混合方法的研究。其中,建立统一的建筑碳排放数据库和开展碳排放不确定性分析是影响评估精度的关键。

乡村公共体育服务供给质量的影响研究——基于体育意识的调节机制

本研究旨在提升乡村公共体育服务供给质量,满足农村居民对体育服务的需求。通过综合前人研究及现状分析,构建农村公共体育服务供给质量概念模型,将场地设施供给、人才供给、财政供给、主体供给、信息不对称作为前因变量,农村公共体育服务供给质量作为结果变量,体育意识作为调节变量。通过德尔菲法、问卷调查法、调节模型检验等方法,深入探索供给内容与农村公共体育服务供给质量的影响关系。研究结果得出:(1)供给内容,即场地设施供给、人才供给、财政供给、主体供给、信息不对称对农村公共体育服务供给质量具有显著影响。(2)体育意识在供给内容与农村公共体育服务供给质量之间具有调节作用。

基于机器视觉的闸坝表面位移非接触式监测方法

针对闸坝表面位移常规监测方法劳动密度高、监测频次低,且难以实现长期稳定监测等问题,提出一种融合时空特征的闸坝表面位移非接触式智能监测方法。该方法采用人工靶标作为标志物,以摄像机为采集设备,通过无线传输图像信息,利用加权分布的自适应伽马修正(AGCWD)与边缘感知因子改进的加权引导滤波(WGIF)增强低照度图像的特征表达能力,通过计算机搭载基于贝叶斯框架的时空上下文信息(STC)算法深度挖掘靶标图像上下文时空信息,进一步地,引入曲面拟合获取靶标的亚像素级位移时程信息,实现闸坝水平和垂直双向表面位移的亚像素级非接触式监测。实验室与现场试验结果表明,不同实验场景下位移监测数据与校验数据高度一致,误差小于0.05 mm;相比于图像优化处理方法,基于AGCWD与WGIF的图像优化处理方法的峰值信噪比提升2.70%,信息熵增加4.91%,标准差降低2.63%;相比于目标追踪算法,基于曲面拟合的STC目标追踪算法的现场监测数据较同类目标追踪算法精度提升48%,可为闸坝表面位移监测提供高精度的解决方案。

生成式人工智能赋能高校思想政治教育的理论基础、现实应用、潜在风险及优化对策研究

生成式人工智能(AIGC)的出现虽然为高校思想政治教育的变革提供了新的技术实现路径,但也存在潜在风险。AIGC在初步理解人类语言、体会情感、模仿思维、提供决策方面为其赋能高校思想政治教育提供可能,并为高校思想政治教育观念、教育内容、教育方法创新上带来了机遇。与此同时存在淡化思想政治教育内容的接受度、泛化思想政治教育目标的明确度、弱化思想政治教育主体地位、虚化思想政治教育载体功能的风险。因此,需要各界协同发力,优化算法推荐,提高思想政治教育内容的精准性;丰富数据来源,夯实思想政治教育的主流价值引导;避免技术滥用,强化思想政治教育的人文关怀;坚持人民至上,探索人机协同的育人模式。