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.

Targeted energy transfer of a parallel nonlinear energy sink

A parallel nonlinear energy sink(NES) is proposed and analyzed. The parallel NES is composed of a vibro-impact(VI) NES and a cubic NES. The dynamical equation is given, and the essential analytical investigation is carried out to deal with the cubic nonlinearity and impact nonlinearity. Multiple time-scale expansion is introduced, and the zeroth order is derived to give a rough outline of the system. The underlying Hamilton dynamic equation is given, and then the optimal stiffness is expressed. The clearance is regarded as a critical factor for the VI. Based on the periodical impact treatment by analytical investigation, the relationships of the cubic stiffness, the clearance, and the zeroth-order attenuation amplitude of the linear primary oscillator(LPO) are obtained.A cubic NES under the optimal condition is compared with the parallel NES. Harmonic signals, harmonic signals with noises, and the excitation generated by a second-order?lter are considered as the potential excitation forces on the system. The targeted energy transfer(TET) in the designed parallel NES is shown to be more e?cient.

A novel method based on entransy theory for setting energy targets of heat exchanger network

A T-Q diagram based on entransy theory is applied to graphically and quantitatively describe the irreversibility of the heat transfer processes.The hot and cold composite curves can be obtained in the T-Q diagram.The entransy recovery and entransy dissipation that are affected by temperature differences can be obtained through the shaded area under the composite curves.The method for setting the energy target of the HENs in T-Q diagram based on entransy theory is proposed.A case study of the diesel oil hydrogenation unit is used to illustrate the application of the method.The results show that three different heat transfer temperature differences is 10 K,15 K and 20 K,and the entransy recovery is 5.498×10~7k W·K,5.377×10~7k W·K,5.257×10~7k W·K,respectively.And the entransy transfer efficiency is 92.29%,91.63%,90.99%.Thus,the energy-saving potential of the HENs is obtained by setting the energy target based on the entransy transfer efficiency.

Highly efficient generation of GGTA1 knockout pigs using a combination of TALEN m RNA and magnetic beads with somatic cell nuclear transfer

The transcription activator-like effector nuclease （TALEN） technique combined with the somatic cel nuclear transfer （SCNT） method has been successfuly applied for creating geneticaly modiifed pigs. However, methods for isolating cels with bialelic indels requires further improvement because of the relatively low enrichment efifciency of mutated somatic cels. Moreover, little is known regarding the off-target effects of the TALEN system and the heredity of TALEN-modiifed pigs. In this study, an efifcient method to increase the enrichment efifciency of TALEN-mediated bialelic knockout （KO） cels was established, and corresponding geneticaly modiifed pigs with the expected genotype were generated whose off-target effect, fertility and heredity characteristics were aslo evaluated. Two TALEN pairs were constructed to target the porcine α-1,3-galactosyltransferase （GGTA1） gene locus. TALEN mRNA was transfected into the ear ifbroblasts folowed by the enrichment of α-Gal nul cels of minipigs using isolectin B4 （IB4） lectin and magnetic beads. A total of 115 cel colonies were formed and validated to beGGTA1 KO cels by sequencing and 10 bialelic KO cel colonies were used as nuclear donors for SCNT. ThirtyGGTA1 bialelic KO piglets were successfuly delivered and grew normaly. Seventeen potential off-target sites were investigated, and no off-target events were detected in the live piglets. To determine the fertility and heredity characteristics of TALEN-modiifed pigs, 10 mature founders were mated with each other and the mutations were determined to be transmitted to the F1 piglets. We established a robust and safe technology for developing geneticaly modiifed pig lines with expected genotypes for agricultural breeding and biomedical application.

Artin Transfer Patterns on Descendant Trees of Finite p-Groups

Based on a thorough theory of the Artin transfer homomorphism from a group G to the abelianization of a subgroup of finite index , and its connection with the permutation representation and the monomial representation of G, the Artin pattern , which consists of families , resp. , of transfer targets, resp. transfer kernels, is defined for the vertices of any descendant tree T of finite p-groups. It is endowed with partial order relations and , which are compatible with the parent-descendant relation of the edges of the tree T. The partial order enables termination criteria for the p-group generation algorithm which can be used for searching and identifying a finite p-group G, whose Artin pattern is known completely or at least partially, by constructing the descendant tree with the abelianization of G as its root. An appendix summarizes details concerning induced homomorphisms between quotient groups, which play a crucial role in establishing the natural partial order on Artin patterns and explaining the stabilization, resp. polarization, of their components in descendant trees T of finite p-groups.

环境目标约束对地方政府清洁生产型行业土地出让的影响

土地资源配置对产业发展具有重要的引导和支撑作用,优化配置工业用地对中国实现发展方式绿色转型发挥着重要的基础性作用。该研究从环境政策与地方领导干部政绩考核相结合的视角,以2007—2020年中国280个地级市的面板数据作为研究样本,分别采用固定效应模型、工具变量法、中介效应模型实证检验了环境目标约束对地方政府清洁生产型行业土地出让的影响及其作用机制,并进一步采用调节效应模型分析了地方政府面临的经济增长压力和财政压力所发挥的调节作用。研究结果表明:①环境目标约束能够显著促进地方政府增加清洁生产型行业土地出让,该结果在经过内生性处理和多种稳健性检验后依然成立。从土地供应政策、产业结构和领导干部特征差异性视角来看,分别在土地供给充裕、工业主导、在任市长非本地来源、在任市长年龄≤55岁的城市,环境目标约束对地方政府增加清洁生产型行业土地出让的促进作用显著。②机制分析发现,环境目标约束会促使地方政府通过提升环境规制强度、加大环境治理监管力度等途径增加清洁生产型行业土地出让。③进一步研究发现,地方政府面临的经济增长压力会削弱环境目标约束对其清洁生产型行业土地出让的促进作用,而财政压力则与环境目标约束的促进作用具有协同作用。以上结论表明中央实施的环境目标“硬约束”政策已促使地方政府在工业用地出让时向绿色产业偏向性配置,为中国实现环境污染治理和资源优化配置的“双赢”提供了重要的现实证据。

技术转移分层靶向机制构建研究

技术成果“转出去”是发挥创新驱动效能与缓解技术转移困境的重要举措,其离不开技术需求管理的有效支撑。研究发现,缺乏对技术需求微观层次属性的关注是导致技术成果转不出去的重要原因。基于分层次管理理论,从技术需求微观层次视角出发,构建以技术需求精细化分层体系为核心,以精准识别、分层靶向、精准反馈、精准管理为关键要点的技术转移分层靶向机制。依托知识产权制度保障与大数据驱动的有效支撑,技术转移分层靶向机制可有效促进技术供需主体精准对接,引导技术创新要素分层次组合与配置,更有效地将技术成果“转出去”。

基于改进YOLOv4网络的红外遥感小目标检测方法

针对传统检测方法对红外小目标检测性能不足的问题,提出一种基于迁移学习与改进YOLOv4网络的红外小目标检测系统。首先,对YOLOv4网络主干网提取的浅层特征进行增强,并结合深层特征与浅层特征来缓解红外小目标难以检测的问题;其次,为YOLOv4网络的检测头模块增加注意力机制,使网络关注于特征图中的红外小目标,从而降低背景对小目标检测的干扰;最终,在YOLOv4网络的训练过程中加入迁移学习方法,从而解决红外小目标标注训练数据不足的问题。基于公开红外小目标检测数据集的实验结果表明,该系统有效提高了YOLOv4网络对红外小目标的检测性能,且优于其他的对比检测模型。

土地出让市场化对城市碳排放绩效的影响研究--基于城市面板数据的检验分析

文章基于2011—2022年中国地级市的面板数据,测算地级市土地出让市场化水平与地级市碳排放绩效,采用空间面板模型、工具变量法及调节效应模型等实证方法,检验了土地出让市场化对城市碳排放绩效的影响及其内在机制。研究发现:土地出让市场化与城市碳排放绩效之间呈显著的“U”型非线性关系,且通过系列稳健性检验与内生性处理后,该结论依然成立;作用机制检验表明,资源配置与产业结构升级在土地出让市场化对城市碳排放绩效的作用机制中发挥中介作用;调节效应检验表明,双重目标约束在土地出让市场化与碳排放绩效关系中发挥显著调节作用。研究结论有助于深入理解土地出让市场化对城市碳排放绩效的影响效果和内在机理,为推进土地出让市场化发展、提升城市碳排放绩效和优化目标约束提供有益借鉴。

基于改进YOLOv7的草莓成熟度检测方法

【目的】为实现自然环境下草莓及其成熟度的高效准确检测,设计了一款改进的YOLOv7的草莓成熟度检测模型。【方法】模型采用YOLOv7作为基础网络对草莓成熟度进行检测。首先,使用PConv卷积替换原Head部分的3×3卷积以提高检测速度。其次,在部分特征层添加CBAM注意力机制提高模型关注重要信息的能力;再将原本上采样中简单的双线性插值算子替换为CARAFE,增加模型对草莓果实细节的感知能力。最后,利用迁移学习实现草莓数据集的训练和微调。【结果】改进前的模型mAP50为85.6%,改进后的模型mAP50为87.7%,参数量从原来的9.14×10^(6)下降为7.32×10^(6),GFLOPs下降为19.8,提高了检测速度。【结论】改进后的模型在草莓及其成熟度的检测中具有更高的检测精度和速度,可以在实际应用场景中实现草莓果实的生长监测。

基于YOLOv5与迁移学习的目标检测和机械臂抓取位姿估计

针对传统机器学习算法视觉识别准确率低、运行时间缓慢等问题,研究针对家庭场景中机器人做家务的场景,利用RGB图像信息为输入,完成对目标物体的抓取位姿估计。以目标检测模型YOLOv5s为基础,利用其轻便、速度快的优点,结合数据增强以及迁移学习搭建网络架构,搭建家庭场景数据集;将少量训练样本进行数据增强后,利用迁移学习将模型在目标数据集上训练,同时微调参数,将目标物体的定位信息通过坐标变换转换为机械臂的抓取位姿,控制机械臂最终以固定抓取姿态完成抓取任务;最后,通过搭建实验平台,操纵UR5机械臂进行实际抓取实验,验证了算法的有效性。提出的基于目标检测的方法速度快、实时性高、误/漏识别率小于2%,应用在机械臂抓取上可以高效地完成任务。

基于深度迁移学习的复杂机场场景飞机目标检测方法

提出了一种改进的深度学习模型,旨在解决检测问题。首先基于迁移学习,微调预训练模型,提高了模型在有限的飞机数据集中的特征提取能力。其次,融入调整模块以增加深层特征图的感受野,提升模型的鲁棒性。引入特征金字塔网络,融合不同尺度的特征信息,进一步增强多尺度特征提取能力。最后,优化了检测头,融合轻量化的分类和回归并行分支,平衡了目标检测的准确性和实时性。构建了易于拓展的Aeroplane数据集,并对所提方法进行了实验验证。结果表明,所提模型在单架飞机、相互遮挡的飞机和小飞机的检测中平均精度分别提高了4.9%、4.0%和4.4%。所提方法在不同环境下表现优于其他经典方法,包括各类遮挡和夜间、雾天等复杂场景,具有良好的场景鲁棒性。

YOLO V4模型在含硫井站火焰和烟雾检测中的应用

针对含硫天然气中H2S等腐蚀性物质易导致井站设备、管线等发生泄漏,易引发火灾,但常用的火焰和烟雾检测仪器、算法易受井站复杂环境影响,且含硫井站人工巡检存在一定风险,提出一种基于深度学习目标检测模型的含硫井站火焰和烟雾检测方法。首先,将能在移动端实时检测的YOLO V4目标检测模型先对公开火焰、烟雾数据集进行训练;接着,将训练好的模型采用迁移学习方法对井站火焰、烟雾数据集进行训练,提取井站火焰、烟雾特征;最后,经迁移学习训练后的YOLO V4模型对火焰、烟雾检测的平均精度均值高达99.62%,配合巡检机器人将对含硫井站有更好的火灾预警和救援侦察能力。

钼锝靶件辐照装置堆外自然循环实验研究

钼锝同位素辐照装置的堆外热工水力研究,对于确定靶件的辐照参数和辐照安全具有重要意义,相关结果可为靶件的设计验证和入堆辐照提供技术支持。本文针对自主研发的钼得靶件辐照装置,设计了堆外传热实验台架,开展了钼锝靶件辐照装置堆外传热验证实验,对比分析了不同热流密度、不同模拟靶件外径、不同辐照装置结构等条件下实验段的温度分布。结果表明,辐照装置内导流管的设计可有效提高装置的自然循环能力,证明了所设计辐照装置的安全性。

一种基于VGGish神经网络的水声目标识别方法

水声目标智能识别是水声装备智能化的重要组成部分,深度学习则是实现水声目标智能识别的重要技术手段之一。当前水声目标智能识别经常面临数据集较小带来的训练样本量不足的情况,针对小数据集识别中存在的因过拟合导致模型泛化能力不足,以及输入的水声信号二维谱图样式不统一的问题,文章提出了一种基于VGGish神经网络模型的水声目标识别方法。该方法以VGGish网络作为特征提取器,并在VGGish网络前部加入了信号预处理模块,同时设计了一种基于传统机器学习算法的联合分类器,通过以上措施解决了过拟合问题和二维谱图样式不统一问题。实验结果显示,该方法应用在ShipsEar数据集上得到了94.397%的识别准确率,高于传统预训练-微调法得到的最高90.977%的准确率,并且在相同条件下该方法的模型训练耗时仅为传统预训练-微调方法的0.5%左右,有效提高了识别准确率和模型训练速度。

脉冲束流下车载加速器中子源靶传热特性

为了研究不同脉冲束流频率下车载加速器中子源靶的传热特性及其热动态响应,通过调整脉冲束流的频率或脉宽对背部冷却微通道靶的传热性能进行模拟,并通过分析靶内部结构的热沉积分布,探究车载加速器中子源靶的热负载上限以及制约束流的因素。选用能量为2.5 MeV、峰值流强为10 mA的束流开展模拟,结果表明:当束流占空比保持在3%不变时,靶的最高温度呈周期波动,且存在上下包络;随着束流频率的上升,每个脉冲周期内靶的上包络温度逐渐下降,这是由于每个脉冲期间的热冲击下降所导致的;频率上升时,每个周期内加热时间与冷却时间同时缩短,导致其下包络温度在最开始增大后基本保持不变;靶的最高温度随着束流频率增加出现从锂层到中间层钽层的转移。该研究对于车载加速器中子源的束流参数选择和靶冷却设计具有一定的参考价值。

基于改进YOLOv7的遥感图像飞机目标检测

针对遥感图像背景复杂所带来的检测精度低、小目标特征丢失的问题,提出一种改进YOLOv7的遥感图像飞机目标检测算法。该算法采用迁移学习的策略,微调预训练模型,提升了模型的泛化能力;通过在模型的检测头引入融合注意力机制模块(CBAM),获取上下文语义信息,以增强对遮挡物和小目标的特征提取能力。采用RSOD数据集来验证改进算法的效果,结果表明:改进后YOLOv7算法mAP达到了97.13%,相比原始YOLOv7提高了3.08个百分点,有效提高了遥感图像飞机的检测精度。

基于数据深度增强的钢桥螺栓脱落智能检测方法研究

针对钢桥螺栓脱落病害人工巡检效率低和智能化检测样本数据集不足的问题,提出一种基于数据深度增强的钢桥螺栓脱落智能检测方法。该方法首先以采集的螺栓图像数据集为基础,利用深度卷积生成对抗网络(Deep Convolutional Generative Adversarial Networks,DCGAN)对有限的螺栓图像进行增广;然后将生成的图像与原始图像合并构建增广后的数据集;再将数据集输入到单阶段目标检测网络YOLO(You Only Look Once,YOLO)中,结合迁移学习方法进行模型训练,并对训练后模型的性能进行验证;最后,进行螺栓脱落病害识别。为验证该方法的可行性,对螺栓脱落检测模型进行试验验证,并对不同采集环境下的某钢桥拼接板螺栓脱落病害进行检测。结果表明:DCGAN可有效生成逼真的螺栓图像,且与常规增广方式相比,DCGAN生成的图像质量更高、性能最优;检测模型受拍摄距离、角度及光照强弱影响且对角度最为敏感,控制拍摄距离在1.6 m内、拍摄角度在20°内、外界光线明亮可保证模型性能较优;与常规增广后训练的模型相比,利用生成图像增广后训练的模型检测性能更优且鲁棒性更好;该智能检测方法可以用于螺栓脱落病害检测,且检测精度明显提高。

Data-Driven Design for Targeted Regulation of Heat Transfer in Carbon/Carbon Composite Structure

Targeted regulation of heat transfer in carbon/carbon composite structure is built for cooling electronic device.A three-dimensional data-driven design model coupling genetic algorithm(GA) with self-adaption deep learning for targeted regulation of heat transfer in built structure is proposed.The self-adaption deep learning model predicts the temperature of built structure closer to optimal value in GA model.The distributions of pore and carbon fiber bundles in built structure are optimized by the proposed model.The surface temperature of electronic device in the optimized structures is 19.1%-27.5% lower than that in the initial configurations when the porosity of built structure varies from 3% to 11%.The surface temperature of electronic device increases with an increase in porosity.The built structure with carbon fiber bundles near the surface of electronic device and pore distribution in the middle of structure has a higher heat dissipation capacity compared with that in the initial configuration.Besides,the computation time of the proposed model is less than one tenth compared with that of the traditional genetic algorithm.

基于CCA融合FFT的SSVEP脑机接口分类算法

为解决多目标刺激范式的稳态视觉诱发电位脑电信号识别准确率低和信息传输率低的问题,提出了一种快速傅里叶变换同典型相关分析相结合的方法,通过快速傅里叶变换将信号训练成对应频率的训练模板,并作为参考信号与实时采集的信号进行典型相关分析来计算频率的识别准确率。6名受试者参与并完成了180组实验,在时间窗口长度为1.5 s的条件下,基于快速傅里叶变换-典型相关分析的稳态视觉诱发电位信号识别算法的平均识别准确率为93.98%,比典型相关分析算法提升了14.75%,信息传输率为62.30 bit·min^(-1),比典型相关分析算法提升了55.63%。实验结果表明,快速傅里叶变换-典型相关分析算法性能更优。