Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

镧系金属一氢化物有效芯势研究

采用Cundari和Stevens等推导的有效芯势对镧系金属一氢化物进行了理论计算，以探讨镧系金属元素与氢的相互作用。结果表明所有镧系金属一氢化物基态时理论上是稳定的，最稳定的是SmH,最不稳定的是DyH;键长计算结果显示，基态时镧系金属一招兵买马花物有独立王国 收缩现象发生；红外振动频率理论计算值与实验结果一致；成键轨道中，金属原子轨道的贡献主要是s轨道和d轨道：从CeH至ErH(GdH)例外）随着外层电子的增加s轨道成分逐渐增大d轨道成分逐渐减小；从TmH和LuH（包括GdH)，成键轨道中金属原子轨道的贡献主要是d轨道，约为90％；约大多数镧系金属一氧化物的成键轨道中金属原子轨道f成分小于1％。     

A new family of proximity graphs: Class cover catch digraphs

Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs—class cover catch digraphs (CCCDs). CCCDs are a special case of catch digraphs. Solving the underlying CCP is equivalent to finding a smallest cardinality dominating set for the associated CCCD, which in turn provides regularization for statistical pattern classification. Some relevant properties of CCCDs are studied and a characterization of a family of CCCDs is given.     

用不确定度评价测量结果中的有效自由度的作用

论述了有效自由度的计算方法及有效自由度量值在分析测量结果中的应用。     

纳米MOSFET迁移率解析模型

从玻尔兹曼方程出发，重新计算了纳米MOSFET沟道内的载流子所服从的分布函数，特别是考虑了纳米MOSFET横向电场和纵向电场之间的相互作用，并且以得到的非平衡状态下的分布函数为基础，考虑载流子寿命和速度的统计分布，给出了纳米MOSFET载流子迁移率的解析表达式.通过与数值模拟结果进行比较和分析，该迁移率解析模型形式简洁、物理概念清晰，且具有相当精度. 关键词： 玻尔兹曼方程 纳米MOSFET 迁移率 沟道有效电场     

Yb3+掺杂铝氟磷酸盐玻璃的光谱和激光性能

研究了Yb3 掺杂铝氟磷酸盐 (AFP)玻璃的吸收光谱、荧光光谱 ,测量了Yb3 离子的荧光有效线宽 (Δλeff>5 5nm)以及2 F5 2 能级的荧光寿命 (τmax=2ms)及随掺杂浓度的变化 .应用倒易法计算了Yb3 的发射截面 ,其发射截面可达 0 6 6 82 3pm2 ,且激光增益系数τfσemi达 1 2 89ms.pm2 .评估了Yb3 在AFP玻璃中的激光性能 ,发现其具有较理想的激发态最小粒子数 (0 15 )、饱和抽运强度 (8 3kW cm2 )和最小抽运强度 (1 2 4 5kW cm2 )值及良好的热稳定性 .研究结果表明掺Yb3 氟磷酸盐玻璃是实现高功率超短可调谐激光器的理想增益介质 .