CSP线Q235变形规律及微观组织实验研究

借助Gleeble-1500热模拟机,研究了Q235钢不同温度、不同变形量及不同变形速率等参数对变形抗力及组织的影响规律,为生产实践提供了参考。     

基于成组技术的模具敏捷设计技术研究

模具已公认为是一种低成本生产高精度复杂形面零件最有效的方法。而成组技术已在制造业的很多领域得以应用。据此本文阐述了一种运用成组技术的核心思想来指导模具设计的方法 ,以期实现模具敏捷设计 ;并针对模具的特点提出了一套新的模具零件分类编码系统 ,为进一步的软件开发奠定了理论基础     

Effects of lanthanum on properties of cathode materials and ion implanted spinning rings

The effects of rare earth elements on the structure of cathode materials composed of WC, 663Cu and Ti as well as the performances of spinning rings implanted with this cathode materials were investigated. The results show that rare earth elements can improve the microstructure and compactness of the cathode materials, and elevate the wear resistance and corrosion resistance of spinning rings implanted with these materials. Consequently, the yarn quality is improved significantly.     

碳化硅颗粒增强铝基复合材料的重熔和铸造工艺特征

介绍了SiC颗粒增强铸造铝基复合材料的铸锭重熔、除气和铸造过程的特征。由于颗粒的存在 ,这些工艺过程和普通铝合金有一定的差异。在铸锭重熔时 ,需要控制熔炼温度和对熔体施加搅拌 ,以防止颗粒反应和下沉。普通铝合金采用的熔盐和气体除气工艺不适合于复合熔体的除气精炼 ,而真空除气具有很好的效果。复合熔体具有高的粘度 ,流动性较差 ,在大气自由重力浇注时容易卷入气体而导致铸件气孔缺陷。为了防止铸件缺陷的形成 ,复合材料在大气自由重力浇注下的浇注系统需要设计集气、集渣冒口和阻流口等特殊组元。真空差压浇注工艺大大简化了浇注系统 ,降低了材料消耗 ,同时消除了大气自由重力浇注时容易产生的铸造缺陷。通过该工艺可获得形状复杂、表面光洁、尺寸精确的铝基复合材料精密铸件     

利用扫描声学显微镜对金属-陶瓷复合涂层亚表层相分布的研究

利用声学显微技术这一无损检测手段对等离子喷涂的金属－陶瓷复合涂层进行了亚表层显微结构的观察，通过分析涂层材料的声波信号，得出了金属相和陶瓷相分布区域的结论，并为涂层表面的背散射电子像和断面的金相观察等手段所证实。     

虚拟仪器技术在机械传动系统扭矩和功率测试中的应用

利用虚拟仪器技术,实现了机械传动系统扭矩和功率的自动测量.文章介绍了该测试系统的结构、功能和系统的软硬件构成以及它们的实现方法.这种测量方法以灵活的软件功能代替固化硬件,提高了系统的可靠性和测量精度.     

Approximation of graph edit distance based on Hausdorff matching

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Investigated applications include nearest neighbor classification of graphs representing letter drawings, fingerprints, and molecular compounds as well as hidden Markov model classification of vector space embedded graphs representing handwriting. In many cases, a substantial speedup is achieved with only a minor loss in accuracy or, in one case, even with a gain in accuracy. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.     

基于自适应蚁群遗传混合算法的 PID 参数优化

针对遗传算法易重复迭代、蚁群算法易陷入停滞的缺点,提出基于自适应蚁群遗传混合算法的 PID 参数优化。先用遗传算法获得 PID 参数的初值,再用改进后的蚁群算法自适应调整路径选择概率和信息素更新规则,最终搜索出 PID 参数的最优值。仿真结果表明,对于给定的被控对象,相比于 GA 和 ACS 算法,该算法搜索出的 Kkp、Kki 、Kkd 最优,系统响应时间短,动态性和稳定性佳,说明该方法整定出的 PID 参数值具有最优性。对于其他的控制对象和过程也具有参考价值。     

Feature scalability for a low complexity face recognition with unconstrained spatial resolution

Automatic face recognition (FR) based applications in low computing power constrained systems, such as mobile and smart camera, have become particularly interesting topic in recent years. In this context, we present computationally efficient FR framework underpinning the so-called feature scalability algorithm. The proposed framework aims at implementing robust FR systems under low-computing power restriction and varying face resolution. Key beneficial property of our proposed FR framework based on feature scalability is to require low computational complexity without sacrificing a level of FR performance. To do this, using feature scalability algorithm enables to directly estimate the features (from pre-enrolled gallery images) that are well matched with the feature of an input probe image with different resolution (generally lower resolution) without any complex process. In addition, our method is helpful for relieving storage shortage problem as it does not require a large amount of training and gallery images with different face resolutions. Results show that our proposed feature scalability algorithm can be seamlessly embedded into state-of-the-art feature extraction methods extensively used for FR by achieving impressive recognition performance. Also, according to the results on computational complexity measurement, the proposed method is proven to be useful for substantially saving FR operation time.     

Global Output‐Feedback Stabilization for Stochastic Nonlinear Systems with Function Control Coefficients

This paper investigates the global output‐feedback stabilization for a class of stochastic nonlinear systems with function control coefficients. Notably, the systems in question possess control coefficients that are functions of output, rather than constants; hence, they are different from the existing literature on stochastic stabilization. To solve the control problem, an appropriate reduced‐order observer is introduced to reconstruct the unmeasured system states before a smooth output‐feedback controller is designed using the backstepping method, which guarantees that the closed‐loop system is globally asymptotically stable in probability. This paper combines the related results in the deterministic and stochastic setting and gives the first treatment on the global output‐feedback stabilization for the stochastic nonlinear systems with function control coefficients. A simulation example is given also to illustrate the effectiveness of the proposed approach.