Low-velocity Impact Damage Analysis of Composite Laminates Using Self-adapting Delamination Element Method

On the basis ofa 2D 4-node Mindlin shell element method, a novel self-adapting delamination finite element method is presented, which is developed to model the delamination damage of composite laminates. In the method, the sublaminate elements are generated automatically when the delamination damage occurs or extends. Thus, the complex process and state of delamination damage can be simulated practically with high efficiency for both analysis and modeling. Based on the self-adapting delamination method, linear dynamic finite element damage analysis is performed to simulate the low-velocity impact damage process of three types of mixed woven composite laminates. Taking the frictional force among sublaminations during delaminating and the transverse normal stress into account, the analytical results are consistent with those of the experimental data.

基于MDS和改进SSA-SVM的高速铁路道岔故障诊断方法研究

针对高速铁路道岔设备故障频繁,现场维修工作量大等问题,提出基于多维尺度缩放法(MDS)和改进麻雀搜索算法(SSA)优化支持向量机(SVM)的高速铁路道岔故障诊断模型。首先以ZDJ9道岔转换功率曲线为研究对象,总结现场典型道岔故障类型及故障原因,分别提取道岔功率曲线的时域、频域特征指标以及小波包能量熵,组成特征指标向量;其次采用MDS方法进行多维特征指标的降维优化,建立道岔故障特征指标样本数据库;最后利用改进Circle混沌映射初始化种群,并通过自适应t分布增强麻雀种群的多样性,再以改进SSA算法优化SVM模型中的惩罚因子和核函数方差2个关键参数,构建改进SSA-SVM的道岔故障诊断模型。故障诊断结果表明,本模型的故障诊断正确率高达96.25%,诊断效果优于其他方法,可以为道岔设备的故障维修提供理论依据。

CEEMDAN-WPE-CLSA超短期风电功率预测方法研究

提出了一种结合自适应噪声完全集合经验模态分解、加权排列熵、卷积神经网络、长短期记忆网络和自注意力机制的超短期风电功率预测方法。首先,利用自适应噪声完全集合经验模态分解将原始风电功率时间序列自适应分解为一系列的模态分量,降低原始序列的非线性和波动性;其次,根据加权排列熵计算各模态分量间的相似性并对相似的分量进行重组,以修正自适应噪声完全集合经验模态分解的过度分解问题,使得修正后的模态分量更具规律性;最后,将重组后的分量输入卷积长短期记忆网络进行时序建模,并利用自注意力机制对卷积长短期记忆网络的神经元权重进行重新分配,提高了卷积长短期记忆网络对输入特征不确定性的适应能力。在此基础上,明确了自注意力机制和自适应噪声完全集合经验模态分解、加权排列熵在风电功率预测中的作用机制,以及风电功率信号包含的重要物理信息,证明了自适应噪声完全集合经验模态分解、加权排列熵以及自注意力机制在风电功率信号模态分解和长短期记忆网络隐层输出权重分配中的有效性。

Self-adapting control parameters modifieddifferential evolution for trajectoryplanning of manipulators

Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.

Large eddy simulation of aircraft wake vortex with self-adaptive grid method

A self-adaptive-grid method is applied to numerical simulation of the evolu- tion of aircraft wake vortex with the large eddy simulation （LES）. The Idaho Falls （IDF） measurement of run 9 case is simulated numerically and compared with that of the field experimental data. The comparison shows that the method is reliable in the complex atmospheric environment with crosswind and ground effect. In addition, six cases with different ambient atmospheric turbulences and Brunt V^iis/il^i （BV） frequencies are com- puted with the LES. The main characteristics of vortex are appropriately simulated by the current method. The onset time of rapid decay and the descending of vortices are in agreement with the previous measurements and the numerical prediction. Also, sec-ondary structures such as baroclinic vorticity and helical structures are also simulated. Only approximately 6 million grid points are needed in computation with the present method, while the number can be as large as 34 million when using a uniform mesh with the same core resolution. The self-adaptive-grid method is proved to be practical in the numerical research of aircraft wake vortex.

基于自适应导数-高斯寻峰算法的自动稳谱研究

航空γ能谱仪在高空测量过程中由于本底计数率较低,容易产生稳谱周期长、受温度影响较大等因素造成的谱线漂移问题,严重影响了测量结果的准确性。因此,本文提出了一种自适应导数-高斯联合寻峰算法,以达到航空γ能谱仪在低本底环境下快速寻峰、稳峰的目的。首先对导数寻峰算法的过程进行了优化,算法根据限定条件自动确定峰位道址及其左右边界,实现高斯寻峰算法的自适应变换本底窗宽扣除,然后使用高斯寻峰结果与导数寻峰结果进行对比验证,确保寻峰结果的准确性。实际测量结果表明:自适应导数-高斯联合寻峰算法计算速度快,准确率高,1024道航空γ能谱仪在-20~50℃范围内40K峰实测最大谱漂不超过±3道,为低本底环境下的航空γ能谱准确测量提供了新的稳谱方法。

Determination of Feature Line Equation for Self-adapting Incremental Press Bending

The basie idea and method about determination of the feature line equations and how to apply them to the numerical control of the press bending of panei skins were introduced. Research indicates that it is feasible to realize the self adapting incremental press bending by adopting the feature line equation. The feature line equation, which is based on the database of the status of practical processes, can be adjusted in time, and the forming precision can be improved. It is important to correctly select and reasonably predict the feature line equations to enhance the accuracy of the incremental press bending based on the feature line database and algorithm. The determination of the feature line equation settles necessary data foundation for further research on the database of self-adapting incremental press bending, and it supplies a new clue for the development of self-adapting incremental press bending.

Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem

Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

基于改进哈里斯鹰和B-spline曲线的无人机路径规划研究

针对无人机在动态环境中的全局路径规划问题,提出了一种改进哈里斯鹰优化算法。针对算法后期搜索性能不足等问题,提出自适应混沌和核心种群动态划分策略,提高算法后期的搜索能力;修改哈里斯鹰更新公式,引入黄金正弦策略,提高算法搜索效率;融合自适应动态云最优解扰动策略,提高算法跳出局部极值的能力;针对三维栅格路径规划问题,设置了一种估值函数,通过计算栅格到达终点的代价,帮助算法进行节点筛选,使算法能搜索到更短路径,并针对路径转角不平滑的问题,使用3次B-spline曲线对路径转角进行处理,使路径更适合无人机飞行。通过国际标准测试函数和在不同大小、不同复杂程度的静态、动态栅格地图进行仿真实验。实验结果显示,本文算法相较于对比算法,规划出的路径平均缩短了14.94%、转角数量平均减少了53.31%。

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

自适应球形演化的药物-靶标相互作用预测方法

相较于传统药物的研发,药物-靶标的预测方法能够有效降低成本,加快研发进程,但是在实际应用中存在数据集平衡度低、预测精确率不高等问题。基于此,提出一种自适应球形演化的药物-靶标相互作用预测方法ASEKELM(self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine)。该方法根据结构相似的药物与靶标更易存在相互作用的原理筛选出高置信度的负样本;并且为了解决球形演化算法易陷入局部最优的问题,利用搜索因子历史记忆的反馈机制及群大小线性递减的策略(LPSR),实现全局搜索和局部搜索的平衡,提高算法的寻优能力;然后利用自适应球形演化算法对核极限学习机(KELM)的参数进行优化。在基于黄金标准的数据集上将ASEKELM与NetLapRLS(Network Laplacian Regularized Least Square)、BLM-NII(Bipartite Local Model with Neighbor-based Interaction profile Inferring)等算法进行对比,验证算法的性能。实验结果表明,在酶(E)、G-蛋白偶联受体(GPCR)、离子通道(IC)和核受体(NR)数据集中,ASE-KELM的ROC曲线下面积(AUC)与PR曲线下面积(AUPR)均优于对比算法;且基于DrugBank等数据库,ASE-KELM在预测新药物-靶标对的验证过程中表现良好。

Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

Credit risk is the core issue of supply chain finance. In the supply chain, problems happened in different enterprises can influent the whole to different degrees through transferring, thus statuses of all enterprises and their different influences should be considered when evaluating the supply chain’s credit risk. We examine the characters of supply chain network and complex network, use the local growing complex network to simulate the real supply chain, use cluster analysis to classify the company into several levels;Introducing each level’s self-adaption weight formula according to the company’s quantity and degrees of this level and use the weight to improve the credit evaluation method. The research results indicate that complex network can be used to simulate the supply chain. The credit risk evaluation (CRE) of an enterprise level with bigger note degrees has a greater weight in the supply chain system’s CRE, thus has greater effect on the whole chain. Considering different influences of different enterprise levels can improve credit risk evaluation method’s sensitivity.

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

Self-adaptive one-dimensional nonlinear finite element method based on element energy projection method

The element energy projection （EEP） method for computation of super- convergent resulting in a one-dimensional finite element method （FEM） is successfully used to self-adaptive FEM analysis of various linear problems, based on which this paper presents a substantial extension of the whole set of technology to nonlinear problems. The main idea behind the technology transfer from linear analysis to nonlinear analysis is to use Newton＇s method to linearize nonlinear problems into a series of linear problems so that the EEP formulation and the corresponding adaptive strategy can be directly used without the need for specific super-convergence formulation for nonlinear FEM. As a re- sult, a unified and general self-adaptive algorithm for nonlinear FEM analysis is formed. The proposed algorithm is found to be able to produce satisfactory finite element results with accuracy satisfying the user-preset error tolerances by maximum norm anywhere on the mesh. Taking the nonlinear ordinary differential equation （ODE） of second-order as the model problem, this paper describes the related fundamental idea, the imple- mentation strategy, and the computational algorithm. Representative numerical exam- ples are given to show the efficiency, stability, versatility, and reliability of the proposed approach.

Self-Adaptive Grinding for Blind Tip Reconstruction of AFM Diamond Probe

Blind tip reconstruction(BTR) method is one of the favorable methods to estimate the atomic force microscopy(AFM) probe shape. The exact shape of the characterizer is not required for BTR, while the geometry of the sample may affect the reconstruction significantly. A cone-shaped array sample was chosen as a characterizer to be evaluated. The target AFM probe to be reconstructed was a diamond triangular pyramid probe with two feature angles, namely front angle(FA) and back angle(BA). Four conical structures with different semi-angles were dilated by the pyramid probe. Simulation of scanning process demonstrates that it is easy to judge from the images of the isolated rotary structure, cone-shaped, the suitability of the sample to be a tip characterizer for a pyramid probe. The cone-shaped array sample was repeatedly scanned 50 times by the diamond probe using an AFM. The series of scanning images shrank gradually and more information of the probe was exhibited in the images, indicating that the characterizer has been more suitable for BTR. The feature angle FA of the characterizer increasingly reduces during the scanning process. A self-adaptive grinding between the probe and the characterizer contributes to BTR of the diamond pyramid probe.

The control of welding wire feed self-adaptive mechanism based on fuzzy PID

The welding wire feed mechanism is an important component of welding equipment, both reliability and stabilization are the premise that the welding quality can be ensured. The PID is currently adapted to control the welding wire feed mechanism, although the fuzzy PID has advantage of fast response and adaptation, the precision of fuzzy PID is lower. Accordingly, the fuzzy self-adaptive PID controller was proposed through changing fuzzy input variables and output variables based on variable universe, simple furwtion is adopted as scaling factor, the fuzzy PID controller parameters are adjusted to improve the precision and adjustment range. Simulation results show that control effects of fuzzy self-adaptive PID adopted by the welding wire feed mechanism have good adaptive ability and robustness based on variable universe, the welding experiments indicate that the welding quality met the requirements actually.

Towards a Component Framework for Architecture-Based Self-Adaptive Applications

Self-adaptive software is an efficient way to cope with highly dynamic nature of the environment where it is situated. In this paper, from the perspective of software architecture, we propose a component framework for supporting the architecture-based design and development of self-adaptive applications. It captures some key elements of the research on software architecture and provides more flexible facilities to decouple interacting components. Based on that, a prototype is implemented to demonstrate its feasibility, and at last a case study is presented to illustrate our framework.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

不平衡样本下的SA-YOLO自适应损失目标检测算法

样本不平衡现象是指在数据集中以背景为主的易样本数量较大,而以前景为主的难样本数量过少,即样本存在类间不平衡与难易不平衡问题。现有目标检测算法大多是基于候选区域的两阶段算法或基于回归的单阶段算法,当应用于不平衡样本时无法避免训练中产生的预测框对大量样本过度依赖,从而导致模型过拟合且检测精度低,准确性、泛化性差。为了在不平衡样本下实现高效精准的目标检测,提出一种全新的SA-YOLO自适应损失目标检测算法。(1)针对样本不平衡问题,提出SA-Focal Loss函数,能够针对不同数据集与训练阶段对损失进行自适应调节,以达到平衡类间样本与难易样本的效果。(2)在多尺度特征预测机制下构造CSPDarknet53-SP网络架构,增强困难小目标样本全局特征的提取能力,达到提升难样本检测精度的效果。为验证SA-YOLO算法的性能,分别在样本不平衡数据集与COCO数据集上进行了大量仿真实验。结果表明:相较于现有YOLO系列算法最优指标值,SA-YOLO在不平衡数据集中mAP可达91.46%,提升10.87%,各类目标AP 50提升均在2%以上,有极强的专精性;在COCO数据集中mAP 50提升1.58%,各项指标均不低于最优值,有良好的有效性。