Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.     

Excellent Energy Storage and Charge-discharge Performances in PbHfO3 Antiferroelectric Ceramics

Single phase PbHfO₃ antiferroelectric ceramics were prepared via rolling process. It is revealed that the rolling process can reduce the grain size and increase the bulk density, which lead to the enhanced breakdown strength up to 268 kV/cm versus 219 kV/cm of samples using the conventional solid-state method. As a result, high recoverable energy density of 7.6 J/cm³ with an efficiency of 80.8 % was achieved. Meanwhile, a large current density of 1381 A/cm² and an ultrahigh peak power density up to 170 MW/cm³ were observed under 250 kV/cm. In addition, unique electrical polarization response characteristics at different electric fields and temperature-induced structural phase transitions were also investigated. The energy storage performance and charge-discharge properties of PbHfO₃ were first studied in this communication and all the results indicate that PbHfO₃ ceramic is a promising candidate for pulse power applications.     

Fenton 絮凝工艺去除废水中的有机物试验

为了研究废水中有机物的去除问题，针对某企业含有机物废水进行了对比试验，采用了Fenton 絮凝联合工艺去除废水中的COD（化学需氧量）和浊度，考察了初始pH值、H2O2投加量、H2O2与Fe2+投加物质的量比、反应时间、絮凝剂投加量以及絮凝剂投加时的pH值对COD以及浊度去除效果的影响。结果表明，采用最佳工艺参数组合后，浊度去除率和COD的去除率分别为99.32%和97.27%。     

固体碳直接还原钢铁厂含锌固废的理论与实践

 钢铁厂内的含锌固废数量巨大，同时富含铁、碳等元素，是可循环再利用的二次资源。通过固体碳直接还原ZnO和FeO的热力学分析，发现在非标准状态下，ZnO的还原条件要优于FeO。为此，在实验室条件下设计了含锌固废在不同温度和时间下的还原试验，发现在还原温度为1 423 K、还原时间为15 min以上时，脱锌率可达到90%，而铁的金属化率需要在1 573 K时才能接近90%；在还原温度低于1 423 K时，还原时间对脱锌率的影响较大；在还原温度高于1 473 K时，还原温度对脱锌率影响较小，同时脱锌率可达到95%。这些结果对国内当前处理含锌固废主要工艺(回转窑、转底炉)的操作制度设置具有重要的参考价值，也为如何选择合理的工艺路线，以低投入、低运营成本实现含锌固废的再利用提供了理论依据。     

TMCP工艺下Ti-Mg氧化物冶金钢轧态组织性能

摘要：为研究轧制过程中氧化物粒子对母材组织性能的影响，提出了一种只在再结晶区进行轧制的新工艺路线，利用常规冶炼及氧化物冶炼的实验钢分析其轧态显微组织、力学性能及相变过程。结果表明：在新轧制工艺下，氧化物冶金钢母材显微组织由常规的贝氏体板条束变为针状铁素体，其基体具有良好的力学性能，-80℃冲击韧性达149J；热轧工艺下的组织转变分两阶段进行，初期是针状铁素体的形核和晶粒细化，之后贝氏体相变被限制在针状铁素体板条间构成的区域内。     

A design method of data analytics process for condition based maintenance

Many condition monitoring systems use data analytics processes such as anomaly detection to understand machine conditions. Such data analytics processes have been designed by data scientists. However, domain knowledge is indispensable for designing the process, and data scientists have difficulty in acquiring such knowledge from domain engineers. This paper proposes a design method of data analytics processes and an engineering tool. In this method, data scientists propose hypotheses about each step of the process to domain engineers. Then, data scientists update the process on the basis of feedback from domain engineers. The engineering tool helps data scientists to interact with domain engineers.     

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

This paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L₂‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.     

A practical data-flow verification scheme for business processes

Data in business processes becomes more and more important. Current standard languages for process modeling like BPMN 2.0 which include the data flow reflect this. Ensuring the correctness of the data flow in processes is challenging. Model checking, i.e., verifying properties of process models, is a well-known technique to this end. An important part of model checking is the construction of the state space of the model. However, state-space explosion typically is in the way of an effective verification. We study how to overcome this problem in our context by means of reduction. More specifically, we propose a reduction on the level of the process model. To our knowledge, this is new for the data-flow analysis of processes. The core of our approach are so-called regions of the process model that are relevant for the verification of properties describing the data flow. Non-relevant regions are candidates for reduction of the process model, yielding a smaller state space. Our evaluation shows that our approach works well on industrial process models.