A hybrid material-point spheropolygon-element method for solid and granular material interaction

Capturing the interaction between objects that have an extreme difference in Young's modulus or geometrical scale is a highly challenging topic for numerical simulation. One of the fundamental questions is how to build an accurate multiscale method with optimal computational efficiency. In this work, we develop a material-point-spheropolygon discrete element method (MPM-SDEM). Our approach fully couples the material point method (MPM) and the spheropolygon discrete element method (SDEM) through the exchange of contact force information. It combines the advantage of MPM for accurately simulating elastoplastic continuum materials and the high efficiency of DEM for calculating the Newtonian dynamics of discrete near-rigid objects. The MPM-SDEM framework is demonstrated with an explicit time integration scheme. Its accuracy and efficiency are further analyzed against the analytical and experimental data. Results demonstrate this method could accurately capture the contact force and momentum exchange between materials while maintaining favorable computational stability and efficiency. Our framework exhibits great potential in the analysis of multi-scale, multi-physics phenomena.     

基于压电驱动的微振动主动减振控制系统研究

本文以压电陶瓷促动器构建并联六自由度减振平台，采用NI Compact-RIO实时控制系统和Fx-LMS自适应控制算法进行振动主动控制，对次级通道辨识方法和主动控制算法进行了模拟仿真，并搭建主动减振控制系统进行实验验证。仿真与实验结果表明，设计的主动减振控制系统对于7~50 Hz范围内的低频微振动有很好的控制效果。     

基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法

针对低信噪比(SNR)环境下传统方法对声信号降噪的局限性，提出了一种联合自适应阈值活动语音检测（VAD）算法和最小均方误差对数谱幅度估计（MMSE-LSA）的实时降噪算法。首先，在VAD算法中通过基于能量概率最大值的概率统计来对背景噪声进行估计，对得到的背景噪声进行实时更新并保存；然后，将实时更新的背景噪声作为MMSE-LSA的参考噪声，并对噪声幅度谱进行自适应更新，最后进行降噪处理。通过在真实场景中对四类声信号进行实验，结果表明，该算法在保证对低SNR声信号的实时处理的情况下，相较于传统MMSE-LSA算法，降噪信号的SNR能够提高10~15 dB，且不存在信号过减的情况，可应用于实际工程。     

Disassembly sequence planning using a Flatworm algorithm

Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.     

红外菲涅尔透镜的注射超声辅助成型优化

为了提升聚合物红外菲涅尔透镜的光学性能,以其表面微沟槽的成型质量为目标,提出了一种高效的注射超声辅助成型方法,并对工艺参数进行了综合质量优化。首先分析了超声振动对聚合物的加热和加压效应,设计了一套一模两腔的对比试验模具;接着以红外菲涅尔透镜的调制传递函数MTF和齿形平均高度h为优化质量目标,设计了四步骤的多目标优化流程,通过试验设计、基于BP神经网络的质量目标与注射工艺参数关系建模、基于NSGA-Ⅱ的多目标优化和试验验证进行工艺参数的综合优化。实验结果表明:该多目标优化流程具有很高的精度,MTF和h的平均预测误差MPE分别为4.16%和3.32%;注射超声辅助成型的菲涅尔透镜微沟槽具有更高的复制质量,其齿沟槽平均高度h增加了15.6%,且h值的波动量随着h值的增大而增大,MTF值受齿高h均匀性的影响大于齿高h对其的影响。     

基于自适应采样算法的芳烃异构化代理模型

异构化是芳烃生产中的重要环节，提高异构化环节的建模和优化效率对工业生产有着重要意义。但是，直接使用机理模型的优化过程耗时较长，优化效率低。代理模型可以有效地对机理模型进行近似，而代理模型采样方法对模型精度有很大影响。提出了一种新的基于稀疏度和最邻近期望的自适应采样算法，该方法可以平衡全局搜索和局部搜索，通过求解优化问题找到反映函数关键信息的新采样点，再加入原始样本集中，使得代理模型精度不断提高。多个测试函数结果表明，相比于其他自适应采样算法，该算法能有效提升代理模型精度和建模效率。该算法在芳烃异构化环节代理模型中也得到了有效验证，与本文中其他算法对比，该算法模型误差减少5%以上，建模时间缩短30%以上。     

Comparison of stress evolution under TGO growth simulated by two different methods in thermal barrier coatings

The growth of thermally grown oxide (TGO) is a significant factor affecting the failure mechanism of thermal barrier coatings (TBCs) during cyclic high temperature service. In this work, a complicated finite element model with two semicircles reflecting the undulation of TGO interfaces was proposed, and four representative shapes of TGO interfaces were selected. There are mainly two methods to simulate TGO growth under high temperature, and each method was achieved by implementation of user subroutines in finite element method. A total of 100 thermal cycle loads were applied to the TBCs continuously. The stress evolution in the layers of Top Ceramic Coating (TC) and Bond Coating (BC) at the end of each thermal cycle load was obtained, the influence of TGO growth on stress evolution was analyzed, the differences between two methods of TGO growth were discussed. The results show that under TGO growth simulated by the first method, the stress distribution in the y direction does not change in both TC and BC layer, and the maximum stress decreases a lot in TC layer but nearly remains the same in BC. When the growth of TGO was simulated by the second method, stress evolution is complex and undergoes up to five stages with a small undulation or convex of TGO interfaces. Stress evolution in BC layer remains as the same as in the first method. Moreover, the maximum stress increases continually in BC layer. The comparison of these two simulation method would help to study the failure of TBCs caused by TGO growth.     

Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression

ABSTRACT

Hydrological processes are hard to accurately simulate and predict because of various natural and human influences. In order to improve the simulation and prediction accuracy of the hydrological process, the firefly algorithm with deep learning (DLFA) was used in this study to optimise the parameters of support vector for regression (SVR) automatically, and a prediction model was established based on DLFA and SVR. The hydrological process of Huangfuchuan in Fugu County, Shanxi Province was taken as the research object to verify the performance of the prediction model, and the results were compared with those by the other six prediction models. The experimental results showed that the proposed prediction model achieved improved prediction performance compared with the other six models.     

Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.