Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Solution for the general integral‐type nonlocal plasticity model with Tikhonov regularization

A new solution approach, based on Tikhonov regularization on the Fredholm integral equations of the first kind, is proposed to find the approximate solutions of the strain softening problems. In this approach, the consistency condition is regularized with the Tikhonov stabilizers along with a regularization parameter, and the internal variable increments are solved from the resulting Euler's equations. It is shown that, as the regularization parameter is increased, the solutions converge to a unique one. A nonlocal yield condition and a nonlocal return mapping algorithm are proposed to carry out the integration of constitutive equations in the time and spatial domains. A global plastic dissipation principle is proposed to relax the classical local plastic dissipation postulate. Numerical examples show that the proposed approach leads to objective, mesh‐independent solutions of the softening‐induced localization problems. A comparison of the results from the proposed approach with those from the gradient‐dependent plasticity model shows that the two models give close solutions.     

质量和成本约束下CSP-T和Spk集成过程控制方案

多水平连续抽样检验方案(CSP-T)是在线过程质量控制工具，但该方案的第一类风险和第二类风险都较高，且不能满足成本约束。提出了CSP-T和过程良率指数(S_pk)集成过程控制方案，该集成方案在满足质量约束的同时，以最小成本运行，并将两类风险控制在既定水平。建立了极限检验能力下的最优CSP-T方案，依据数据的计数特征驱动质量控制方案运行。基于S_pk估计的精确分布建立了风险控制方案，依据数据的计量特征驱动风险控制方案运行。质量控制方案和风险控制方案是独立互补关系。相比于CSP-T方案，检验工作量没有增加。企业案例验证了集成控制方案的可行性和有效性。     

Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

海外工程自动化项目的制约条件及应对措施

通过总结多个海外工程项目的投标经验，结合巴西工程，揭示了国内工程公司海外工程自动化项目的制约条件，并提出合理的应对措施，以期在今后的海外工程项目中提升国内工程公司的竞争实力。     

危机矿山问题的对策研究

危机矿山问题 ,是一个涉及矿产资源勘查与开发政策、矿业发展战略、国家资源安全和社会经济可持续发展的重要问题。本研究提出“实施资源接替勘查工程 ,加大矿业治理整顿力度 ,加大科技攻关力度”的三项建议 ;认为开展资源、矿业、矿山开发程度与发展潜力的调查与评价 ;编制我国资源矿产总体规划 ;通过国家适当政策扶持 ,发展科学发展观指导下的矿产资源的开发利用机制 ,是解决危机矿山问题最基本的工作基础和前提     

海外工程自动化项目的制约条件及应对措施

通过总结多个海外工程项目的投标经验,结合巴西工程,揭示了国内工程公司海外工程自动化项目的制约条件,并提出合理的应对措施,以期在今后的海外工程项目中提升国内工程公司的竞争实力。     

一种增广拉格朗日优化方案及其非连续变形分析实现

接触力计算精度是关乎非连续变形分析结果有效性的关键因素之一。经典非连续变形分析法(DDA)采用罚法施加块体间的接触约束,实现简单,但合理的罚值选取困难。因此,提出了一种增广拉格朗日优化算法,改进DDA中接触约束的处理。通过结合开闭迭代算法和自适应罚值更新方案等,提出并实现了多面体DDA增广拉格朗日算法及其优化方案。最后,设计了2个经典数值算例,计算结果表明:改进后的三维DDA提高了计算精度且保证了计算效率,可用于复杂多面体块体系统的非连续力学行为分析,如大型节理岩体工程稳定性分析。     

作为复合行政行为的城市规划

在合法的私有财产保护入宪和物权法实施后,城市规划的法律控制一直是学界广泛关注的课题。但由于对所编制的城市规划的法律性质认识的不一致则产生了不同的结论。本文借用行政法中的基本理论——行政行为,来分析城市规划的法律职能。本文先从抽象行政行为与具体行政行为开始辨析,认为城市规划是具有具体行为特征的抽象行政行为。如果从具体的行政行为来分析,城市规划则是一种兼有行政指导、行政给付、行政强制、行政征用等特性的复合行政行为。但城市规划的这些行为是"不成熟"或者是规制性的行政行为。对城市规划行政行为的分析,目的是能够有效地对城市规划进行法律控制。