As,Sb,Sn对30CrMnSiA钢力学性能的影响

在３０ＣｒＭｎＳｉＡ钢中加入不同量的Ａｓ，Ｓｂ，Ｓｎ元素，研究了这些元素单独存在及复合存在时对钢的强度，塑性，冲击韧度及韧脆转变温度的影响规律，并用扫描电镜及俄歇能谱分析了Ａｓ，Ｓｂ，Ｓｎ导致晶界脆化的机理。     

PD275kg／m全长热处理钢轨研制

以高碳低锰碳素热轧钢轨为热处理原料轨，采用双频电感应加热、压缩空气欠速淬火热处理技术研制的ＰＤ２７５ｋｇ／ｍ热处理钢轨，硬化层深度≥１５ｍｍ、组织为细珠光体；力学性能：σ０．２８１５￣９８０ＭＰａ、σｂ１１８５￣１３４０ＭＰａ、δ５１０％￣１５％，常温冲击韧性ａＫ１４￣３３Ｊ／ｃｍ＾２，断裂韧性ＫＩｃ平均值４５．５ＭＰａ·ｍ＾１／２。具有强度高，塑韧性好的综合性能。使用性能优良，寿命比同曲线的Ｕ７     

裂纹对石墨烯拉伸力学性能影响的数值仿真

为研究裂纹对石墨烯力学性能的影响,以锯齿型石墨烯为研究对象,基于分子动力学方法,采用Tersoff势函数分析裂纹长度对石墨烯拉伸力学性能的影响以及含有裂纹的石墨烯在不同应变率下的拉伸变形破坏过程.结果表明,裂纹长度的增加大大减小石墨烯的抗拉强度和抗拉应变,对弹性模量有一定影响;裂纹降低石墨烯的抗拉应变对应变率的敏感性,但对于含有裂纹的石墨烯,仍可以增大加载速率来提高石墨烯的抗拉性能.     

用于多峰函数优化的小生境差分演化算法

针对监控视频图像的特点,提出了一种有效的实时视频降噪算法。首先结合多帧图像采用基于Non—localmeans的运动检测方法自适应地区分图像的运动区域和静止区域,对静止区域采用时域加权均值滤波,对运动区域采用空域ANL滤波。充分利用了视频的时域、空域信息,在去除视频序列噪声的同时很好地保护了图像的细节。实验结果表明,提出的算法在不造成运动拖影的前提下,能够显著提高视频的信噪比和图像的主观质量。     

基于互补原理确定黄土高原Budyko方程的流域特性参数

Budyko方程的流域特性参数一般根据植被覆盖度等下垫面特征进行确定,但相关资料不易获取。本文针对黄土高原地区29个小流域,通过分析流域特性参数、植被覆盖度和互补关系中大气湿润指数之间的关系发现,大气湿润指数能够间接反映植被覆盖度的大小,进而能够利用常规气象数据确定流域特性参数,并拟合出了经验公式。相比采用固定流域特性参数,这种方法能够提高Budyko方程对实际蒸散发量的模拟效果。相比于植被覆盖度等下垫面资料,常规气象数据时间序列长、且更易获取,因此本方法具有较好的应用前景。     

Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.