Regularization Method to the Parameter Identification of Interfacial Heat Transfer Coefficient and Properties during Casting Solidification

The accurate material physical properties, initial and boundary conditions are indispensable to the numerical simulation in the casting process, and they are related to the simulation accuracy directly. The inverse heat conduction method can be used to identify the mentioned above parameters based on the temperature measurement data. This paper presented a new inverse method according to Tikhonov regularization theory. A regularization functional was established and the regularization parameter was deduced, the Newton-Raphson iteration method was used to solve the equations. One detailed case was solved to identify the thermal conductivity and specific heat of sand mold and interfacial heat transfer coefficient (IHTC) at the meantime. This indicates that the regularization method is very efficient in decreasing the sensitivity to the temperature measurement data, overcoming the ill-posedness of the inverse heat conduction problem (IHCP) and improving the stability and accuracy of the results. As a general inverse method, it can be used to identify not only the material physical properties but also the initial and boundary conditions' parameters.

Application of orthogonal experimental design and Tikhonov regularization method for the identification of parameters in the casting solidification process

The inverse heat conduction method is one of methods to identify the casting simulation parameters. A new inverse method was presented according to the Tikhonov regularization theory. One appropriate regularized functional was established, and the functional was solved by the sensitivity coefficient and Newtonaphson iteration method. Moreover, the orthogonal experimental design was used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iteration and improve the identification accuracy and efficiency. It illustrated a detailed case of AlSiTMg sand mold casting and the temperature measurement experiment was done. The physical properties of sand mold and the interracial heat transfer coefficient were identified at the meantime. The results indicated that the new regularization method was efficient in overcoming the ill-posedness of the inverse heat conduction problem and improving the stability and accuracy of the solutions.

REGULARITY PROPERTIES OF AN IDENTIFICATION PROBLEM FOR GEOTHERMAL RESERVIOR EXPLOITATION

The problem of determining the pass on heat coefficient of the water-bearing stratum in geothermal reservior exploitation is investigated using the regularised output-least-square formulation. The regularity properties of the coefficient is obtained.

Identification of Rotor Unbalance as Inverse Problem of Measurement

In this paper, the problem of identification of the characteristics of the rotor unbalance on two supports is investigated as the inverse problem of measurement. The vibration of rotor supports in two mutually perpendicular directions used as the initial information. The inverse problem is considered, taking into account the error of the mathematical description of rotor-bearings system. To obtain estimates of real unbalance characteristics, the hypothesis as to the exact solutions is applied. The method of Tikhonov regularization is used to obtain stable results. Test calculations are given to illustrate the proposed approach.

Wavelet packet-based identification of complex oscillation in biological signals

Owing to the intrinsic nonlinearities of the system,a contracting mechanism,such as myogenic response,may induce different oscillatory patterns.Many specialists discussed the relations of oscillatory patterns with intrinsic control system or some pathological condition,but there is no single,well-defined criterion to achieve the identification of regular,stochastic,and chaotic activities.In this paper,we focus on the Mallat algorithm of wavelet packet and use it in the identification of the regular periodic,stochastic,and chaotic fluctuations.According to the specific frequency configuration of the chaos activity,we select proper layers of decomposition of wavelet packet and did fine segments to the frequency of signals.The frequency band of energy convergence could be recognized.The signal of periodic,stochastic,and chaotic could be distinguished depending on it.Numerical experiment is given to show its efficiency.Experiments on 12 babies' lung data have been done.This identification by means of wavelet packet could support the cardiologist or cerebral specialist to do more observation and deeper analysis to physic signals.

Joint input–output identification of unstable systems with kernel regularization

This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems,which can be tackled by using kernel-based regularization methods.We propose to identify two transfer functions by kernel regularization,the one from the reference signal to the input,and the one from the reference signal to the output.Since these transfer functions are stable,kernel regularization methods can construct their accurate models.Then the model of unstable system is constructed by ratio of these functions.The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.

Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring conditions.Although l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased estimator.To address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge effectively.To mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity strategy.Then,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated parameters.The effectiveness and performance of the proposed method are validated on an aircraft skin-like composite structure.Additionally,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is conducted.It turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization method.The impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

Identification of Mathematical Model Parameters of Stationary Process

In work the problem of synthesis of mathematical model of stationary process is examined in deterministic statement. It is supposed that the amount of measurements by each variable minimally and coincides with number of variable in model. The some possible variants of statement of such problem are considered. The calculations on real measurements were executed for comparison with known methods.

Regularized automatic frequency response function acquisition of a milling robot operating in a high-dimensional workspace

Because robotic milling has become an important means for machining significant large parts,obtaining the structural frequency response function(FRF)of a milling robot is an important basis for machining process optimization.However,because of its articulated serial structure,a milling robot has an enormous number of operating postures,and its dynamics are affected by the motion state.To accurately obtain the FRF in the operating state of a milling robot,this paper proposes a method based on the structural modification concept.Unlike the traditional excitation method,the proposed method uses robot joint motion excitation instead of hammering excitation to realize automation.To address the problem of the lack of information brought by motion excitation,which leads to inaccurate FRF amplitudes,this paper derives the milling robot regularization theory based on the sensitivity of structural modification,establishes the modal regularization factor,and calibrates the FRF amplitude.Compared to the commonly used manual hammering experiments,the proposed method has high accuracy and reliability when the milling robot is in different postures.Because the measurement can be performed directly and automatically in the operation state,and the problem of inaccurate amplitudes is solved,the proposed method provides a basis for optimizing the machining posture of a milling robot and improving machining efficiency.

双环醇、联苯双酯、五味子丙素的UHPLC-ESI-Q-TOF-MS分析及其应用

目的:分析双环醇、联苯双酯和五味子丙素的质谱裂解规律,鉴定双环醇原料药中的杂质。方法:采用超高效液相色谱-电喷雾-四极杆-飞行时间质谱法,以电喷雾电离源为离子源,在正离子模式下,利用飞行时间质谱对双环醇、联苯双酯和五味子丙素的准分子离子及其特征碎片离子信息进行分析,根据碎片质量变化推测可能的裂解途径,分析总结其质谱裂解规律;双环醇原料药样品首先通过液相色谱分离,寻找其中的杂质峰,然后利用质谱对杂质峰进行分析,结合裂解规律辅助鉴定杂质的结构。结果:3种化合物在正离子模式下均产生[M+H]+的准分子离子,经碰撞诱导解离后发生C—O键、C—C键的简单断裂以及氧环的开环断裂,并伴随以CH2O为主,CO2、CO、CHO为辅的中性碎片丢失,解离均集中在中高质量区。3种化合物只有支链结构和/或氧环结构发生C—C、C—O键的简单断裂,而联苯母核结构未曾改变。其中,双环醇由于含有苄位羟基结构,故在酸性流动相条件下,会以[M+H-H2O]+的形式稳定存在;五味子丙素因其含有八元环结构,故碰撞电压下率先出现开环,继而再出现中性碎片丢失。双环醇原料药中杂质的二级质谱与联苯双酯的二级碎片表现出的质谱裂解行为基本一致。结论:本研究归纳了3种五味子衍生物的质谱裂解规律。双环醇原料药中出现的杂质可能为联苯双酯。

Impact Force Magnitude and Location Recognition of Composite Materials

In order to identify the location and magnitude of the impact force accurately,determine the damage range of the structure and accelerate the health monitoring of key components of the composite,this paper studies the location and magnitude of the impact force of composite plates by an inverse method.Firstly,a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force,which is from several impact locations,and establish transfer functions between the impact location and the PZT sensor.Secondly,this paper applies several forces to any location on the material plate,and collects the corresponding response signals,and reconstructs the impact force of several locations in turn.Finally,according to the reconstruction result of each location,the correct impact location is identified.Then,an improved regularization method is used to optimize the reconstructed impact force and accurate the magnitude of the impact force.The comparison experiments prove that the recognition error of this method is smaller.

基于空间稀疏先验的冲击载荷识别频域非凸稀疏正则化方法

复合材料因其强度高、刚度高、密度小等优点而广泛应用于航空、航天等领域。但由于其抗冲击性差,监测作用于复合材料结构上的冲击载荷对于结构快速检测损伤十分重要。经典的Tikhonov正则化方法在欠定情况下求解载荷识别问题时容易在载荷非加载区识别出虚假力;近年来兴起的L1范数稀疏正则化方法在识别冲击载荷时常低估载荷的幅值。为了突破这些方法的局限以实现更高精度的冲击载荷识别,该研究基于冲击载荷的空间稀疏先验,提出一种新的冲击载荷识别频域非凸稀疏正则化方法。所提出的方法结合了广义极小极大凹惩罚项的非凸优势以及非凸保凸的特性,利用向前向后分裂算法进行凸优化求解,既避免了非凸优化容易收敛到局部最优解的问题,又促进了解的稀疏。分别在复合材料梁和复合材料层合板上开展了冲击载荷试验验证,试验结果表明,无论在正定还是欠定的情况下,所提出方法能在精准定位冲击位置的同时重构冲击载荷的时间历程,且该方法在促进解稀疏和识别载荷幅值方面的表现都优于L1正则化方法,其中幅值识别精度能比L1正则化提升50%以上。

基于混合正则化方法的结构载荷识别与响应重构

针对结构载荷识别与响应重构问题中存在的不适定性,提出一种最小平方残差(least square minimal residual,LSMR)算法与Tikhonov正则化方法相结合的混合正则化方法,实现利用结构有限测点的加速度响应识别载荷并重构未知的各类型响应。首先,基于时域状态空间模型构建结构的传递矩阵,并建立载荷识别与响应重构方程;其次,采用混合正则化方法改善载荷识别问题的不适定性,得到载荷的正则化解,并结合响应重构方程的传递矩阵对结构的位移、速度和加速度响应进行重构;最后,通过简支梁数值仿真和试验分析验证所提方法的可行性。结果表明,所提方法能改善重构方程的不适定性,从而对结构未知载荷和各类型响应进行有效重构。

我国中小学教师流动的问题及对策——日本教师“定期流动制”的启示

教师的合理流动一直是促进基础教育均衡发展的关键,是实现教育振兴的必经之路。日本在基础教育阶段推行的教师“定期流动制”表现出流动的强制性、流向的多样性以及流动过程的规范性等特点,对于教育的稳定、教育质量的提高、教师的专业发展起到了积极的促进作用。基于此,为了解决当前我国中小学教师流动过程中存在的问题,应采取健全和完善教师流动制、建立主体多方受益的保障机制、采取积极的差异化策略以及注重宣传教育,增强教师流动的义务感等措施妥善解决,以推动我国中小学教师的合理流动。

船体梁总纵弯曲载荷识别方法及试验验证

基于结构应变动态响应,采用时域反卷积算法建立了船体梁总纵弯曲载荷识别理论模型.结合Tikhonov正则化理论及FFT(快速傅里叶变换)滤波算法,有效提高了载荷识别精度.并采用系列箱型梁结构模型动载试验验证了载荷识别理论模型的有效性.结果表明:建立的载荷识别理论模型可以准确地反演得出施加载荷时程曲线,误差小于5%.

基于有约束L_(1/2)范数稀疏正则化的声源识别方法

基于等效源法(equivalent source method,ESM)的近场声全息(near field acoustic holography,NAH)是一种有效的声源识别技术。然而,针对空间稀疏分布的声源识别问题,传统基于L_(2)范数以及基于L_(1)范数的ESM方法分别存在声源幅值被低估与算法稳定性差等问题。因此,提出了基于有约束L_(1/2)范数稀疏正则化的声源识别方法,该方法具有强稀疏性与强抗干扰的优势,可以解决传统方法的声源识别精度低的问题。通过数值模拟试验以及普通室内的实测实验,验证了方法的有效性。

融合注意力的教师互一致性半监督医学图像分割

医学图像分割在疾病辅助诊断中起着关键的作用。现有的深度分割模型需要依赖带有标注的数据完成大规模训练,而医学影像标注需要具有专业背景的临床医生进行像素级标注,导致标注数据获取困难。基于半监督的医学图像分割方法利用少量的标注数据和大量的未标注数据进行学习,可以在一定程度上缓解标注数据获取困难的问题。针对半监督分割模型不能充分利用未标注数据中的可学习信息的问题,提出一种半监督分割模型TCA-Net。该模型使用U-Net作为骨干网络,通过在U-Net中引入卷积块注意力模块(CBAM)与多头自注意力模块(MHA)来解决其在下采样过程中的信息丢失问题;为了充分利用未标注数据中的不确定性信息,构建一个教师互一致性模型,该模型由具有1个编码器和3个略有不同的解码器的学生模型与教师模型组成,通过在学生模型的概率映射与教师模型的伪标签之间添加一致性约束,以此在训练过程中最小化输出之间的差异,从而提升模型的分割效果。在公开的WORD腹部多器官数据集与ACDC心脏数据集上进行实验,结果表明,在使用20%标注数据的WORD数据集上,TCA-Net的Dice系数、Jaccard指数、HD95和ASD分别达到90.81%、83.79%、21.38和6.08,在ACDC数据集上分别达到89.69%、81.94%、1.66和0.45。消融实验与对比实验结果表明,TCA-Net能够有效提升未标注数据的利用率,在不同数据集上均达到了较好的分割效果,验证了模型的鲁棒性。

职业院校“双师型”认定工作的比较研究

从“双师型”教师概念的历史沿革入手,通过省级“双师型”教师认定政策的比较分析,发现“双师型”教师认定存在标准定位模糊、配套政策匮乏、企业主体缺位、认期管理不足等问题,据此提出系统化思维、精细化理念、共同体意识、特色化布局四个方面的思考,为职业院校推进“双师型”教师队伍建设提供借鉴参考。

基于联合仿真的飞机着陆机场跑道桥动载特性研究

为研究飞机着陆滑跑过程中冲击作用对机场桥梁动力响应的影响,以某拟供C类飞机使用的跑道桥为研究对象,以Boeing 737-800机型为例,首次基于有限元软件与多体动力学软件相结合的联合仿真技术建立了机-桥耦合模型,精细化模拟了飞机以不同的着陆姿态在桥梁上着陆滑跑的全过程。在验证机-桥耦合模型准确性的基础上,系统讨论了飞机着陆质量、下沉速度、俯仰角及滚转角等参数对桥梁动力响应的影响程度、影响规律,并明确了桥梁动力系数的分布范围。研究结果表明:飞机着陆冲击作用下,桥梁动力系数的主要分布区间为1.26~1.62;桥梁动力系数随着陆质量、下沉速度、滚转角增大而增大,随接地速度、俯仰角增大而减小;飞机下沉速度的影响最为显著,当下沉速度从1.00 m/s增大到3.05 m/s时,动力系数从0.98增大到1.87,增幅高达90%。跑道桥的设计需合理考虑飞机着陆滑跑过程的动载作用。

基于RepVGG-A0改进的公路车型识别网络

针对当前车型识别过程中检测精度与实时性难以平衡的问题,提出了一种基于RepVGG-A0改进的公路车型识别网络,利用结构重参数化思想融合多分枝网络以提升网络推理速度。使用混合空洞卷积替换传统卷积,强化了模型对大目标的识别能力。在网络主干中插入融合残差结构的坐标注意力(RES-CA)模块,提升了网络对有效特征信息的提取能力,同时避免了梯度消失与梯度退化造成的影响。此外采用了标签平滑正则化方法对损失函数进行改进,降低了模型过拟合对检测结果的影响,提升了模型的泛化性。经验证,本方法在公路车辆数据集BIT-Vehicle上的识别准确率达到了97.17%,较原模型提升了2.67%,优于现有的ResNet-18,VGG等网络模型,同时保证了模型的检测速度。