A high-temperature oxidation-resistant Fe-Mn-Al-Si alloy

To develop satisfactory alloys without Cr or Ni for high-temperature application up to 1100C, three alloys based on Fe-10%Al-Si with differing fourth (or fifth) element additions were oxidized in air at 1100°Cfor 24 hr. A low carbon, Fe-30Mn-10Al-Si alloy exhibited excellent high-temperature oxidation resistance. The total weight gain for 24 hr oxidation in air at 1100°C was only 1.03 mg/cm ². After air oxidation for 6 days at 1100°C, no nodule formation or breakthrough oxidation occurred. Post-oxidation SEM and EDAX examination showed that a thin, compact, protective alumina scale formed on the alloy.Visiting Scientist (People's Republic of China).     

INVESTIGATION ON CRYSTALLIZATION KINETICS OF Pd-Cu-Si GLASS

The crystallization kinetics of Pd-Cu-Si glass was studied by means of diferential scanningcalorimetry-Ⅱ.According to Kissinger peak shift meth(?)d and Arrhenius equation,theapparent activation energy was calculated.The crystallization kinetics follows Johnson-Mehl-Avrami equation with n=3.0 within 0.15相似文献   

具备反向学习和局部学习能力的粒子群算法

为解决粒子群优化（Particle Swarm Optimization,PSO）算法中存在的种群多样性和收敛性之间的矛盾,该文提出了一种具备反向学习和局部学习能力的粒子群优化算法（Reverse-learning and Local-learning PSO,RLPSO）。该算法保留了初始种群中满足排异距离要求的多个较差粒子以及每个粒子的历史最差位置。当检测到算法陷入局部最优时,利用这些较差粒子的位置信息指导部分粒子以较快飞行速度进行反向学习,将其迅速牵引出局部最优区域。反向学习过程可改善粒子种群的多样性,保证了算法的全局探测能力;同时,利用较优粒子间的差分结果指导最优粒子进行局部学习与搜索,该过程可与粒子群的飞行过程并行执行,且局部学习的缩放因子可随进化过程动态调节。局部学习可提高算法的求解精度,保证算法的迅速收敛。实验结果表明,RLPSO 算法同其他 PSO 算法相比,在高维函数优化中具有收敛速度快、求解精度高的特点。     

基于混沌时间序列理论的发动机状态参数降噪研究

针对因缺乏未染噪数据而难以考查航空发动机状态参数降噪效果的问题,提出了基于混沌时间序列理论来考查其降噪效果的办法,并结合实例考查了小波、奇异谱分析和局部投影3种降噪方法对航空发动机状态参数的降噪效果.仿真结果表明,局部投影法更多地保留了发动机状态参数的混沌特性,适用于航空发动机状态参数的降噪处理.     

概率最大受限路径相容算法

研究了可用于求解约束满足问题的最大受限路径相容算法(maxRPC).maxRPC算法执行过程中有大量无效的寻找路径相容证明(PC-witness)的操作,有效地识别和避免这些无效的寻找PC-witness的操作,可以提高maxRPC算法的求解效率.首先,提出了在一条约束上任意两个相容的值在任意路径上存在PC-witness的概率;然后,基于这一概率提出了一种概率最大受限路径相容算法(PmaxRPC),并将新算法成功应用于求解约束满足问题的回溯搜索.实验结果显示:PmaxRPC可以避免一部分无效的寻找PC-witness的操作,在求解约束满足问题时,PmaxRPC效率高于maxRPC.在某些测试用例上,PmaxRPC比maxRPC和最流行的弧相容算法效率更高.     

基于混合生物地理学优化的聚类算法

聚类分析是数据挖掘的重要任务之一,而具有易早熟与收敛速度慢等缺陷的传统生物地理优化算法(Biogeography-Based Optimization, BBO)很难满足具有NP(Non-deterministic Polynomial)性质的复杂聚类问题需求,于是提出了一种基于混合生物地理学优化的聚类算法(Mixed Biogeography-Based Optimization, MBBO),该算法构造了一个基于梯度下降局部最优贪心搜索的新迁移算子,以聚类目标函数值作为个体适应度进行数据集内隐簇结构寻优。通过在4个标准数据集(Iris、Wine、Glass与Diabetes)的实验,结果表明MBBO算法相对于传统的优化算法具有更好的优化能力和收敛度,能发现更高质量的簇结构模式。     

基于局部复杂度的环境遮挡

为了提高基于屏幕空间的环境遮挡算法(SSAO)的渲染速度,利用局部复杂度的帧间连续性,提出一种SSAO加速算法.首先为每个像素定义局部复杂度,其中局部复杂度相似的像素会有近似的环境遮挡(AO)值;在此基础上,利用帧间相关性,对于当前帧的像素p,若能在前几帧中找到与它有近似局部复杂度的某个像素,则可以直接复用前面帧的像素的AO值而无需重新计算;最后针对AO复用过程中产生的累积误差提出一种避免误差的方法.实验结果表明,在不影响效果的情况下,该算法可以将SSAO的渲染速度提高10%~40%.     

基于mbed的无线体重计快速设计

基于ARM公司的ARM mbed IoT云端软件平台和意法半导体推出的Nucleo开发套件,将HX711采集到的压感数据进行采集和处理,得到体重数据后,通过蓝牙BLE的GATT协议将相应数据进行传输,在安卓智能设备端编写支持蓝牙BLE的APP,接收并显示体重数据,这种无线蓝牙体重计原型的快速开发和验证显示了mbed平台易于开发和高效便捷的特性.     

Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems

Evolutionary algorithms (EAs) are often employed to multiobjective optimization, because they process an entire population of solutions which can be used as an approximation of the Pareto front of the tackled problem. It is a common practice to couple local search with evolutionary algorithms, especially in the context of combinatorial optimization. In this paper a new local search method is proposed that utilizes the knowledge concerning promising search directions. The proposed method can be used as a general framework and combined with many methods of iterating over a neighbourhood of an initial solution as well as various decomposition approaches. In the experiments the proposed local search method was used with an EA and tested on 2-, 3- and 4-objective versions of two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). For comparison two well-known local search methods, one based on Pareto dominance and the other based on decomposition, were used with the same EA. The results show that the EA coupled with the directional local search yields better results than the same EA coupled with any of the two reference methods on both the TSP and QAP problems.