On the Support Problem for Drinfeld Modules

Let Φ be a Drinfeld A-module over an A-field K of generic characteristic. We will prove the following two results which are analogous to ones in number fields. Case 1. Φ is of rank one. Suppose that P and Q are two nontorsion points in Φ(K). If for any element a ? A and almost all prime ideals 𝒫 in  one has that Φ _a (P) ≡ 0 (mod 𝒫) ? Φ _a (Q) ≡ 0 (mod 𝒫), then Q = Φ _m (P) for some m ? A. Case 2. Φ is of general rank ≥ 1. Let x, y ? Φ(K) be two K-rational points. Denote  = End _K (Φ) which is commutative and Λ =  · y which is a cyclic -module. Let red _v :Φ(K) → Φ(k _v ) be the reduction map at a place v of K with residue field k _v . If red _v (x) ? red _v (Λ) for almost all places v of K. Then f(x) = g(y), for some nonzero elements f and g in .     

Bidding strategy with forecast technology based on support vector machine in the electricity market

The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection risk is defined. The other takes into account the impact of the producer’s own bid. The risks associated with the bidding are controlled by the parameter settings. The proposed approaches have been tested on a numerical example.     

基于SVM回归模型的混合气体组分种类光谱识别方法

针对混合气体红外光谱分析中无法采用同一模型同时进行混合气体组分浓度的定量分析和组分种类的定性分析的问题,本文提出了基于SVM回归模型的混合气体组分种类光谱识别方法.通过详细推导,证明混合气体组分种类识别完全可以通过组分浓度分析的SVM回归模型来求解,混合气体组分种类识别是一种特殊的回归.实验结果显示,该方法的混合气体组分种类的正确识别率不小于92.5%.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

基于计算机视觉技术的茶叶品质随机森林感官评价方法研究

为弥补茶叶品质感官审评存在的缺陷，利用计算机视觉技术对茶叶品质进行快速无损评价研究。以碧螺春绿茶为对象，依据专家感官审评结果，将茶样分成4个等级；采用中值滤波及拉普拉斯算子对茶样图像进行预处理，并提取预处理后的茶样图像的颜色特征和纹理特征以表征茶叶图像的外形特征，利用随机森林算法对茶叶外形特征属性进行重要性排序；筛选出重要性较大的特征及随机森林算法中最优的决策树棵数建立感官评价模型，并与建立的支持向量机(SVM)模型性能相比较。结果表明：色调均值、色调标准差、绿体均值、平均灰度级、饱和度均值、红体均值、饱和度标准差、亮度均值、一致性等9个特征属性的重要性较大，且与感官审评特征描述结果相一致；当采用优选出的9个重要性较大的特征及决策数棵数为500时，建立的模型性能最优，模型总体判别率为95.75%，Kappa系数为0.933，OOB误差为5%，较SVM模型分别提高了3.5%，0.066，优选的9个重要性较大的图像特征与感官审评特征描述相一致。研究表明：利用随机森林方法筛选出对茶叶外形特征属性贡献最大的少数几个特征建立模型，模型性能就能达到很好的识别效果，模型得到简化，同时模型精度和稳定性都高于其他方法。     

CO oxidation over ceria supported Au22 nanoclusters: Shape effect of the support

CO oxidation over ceria-supported Au₂₂ nanoclusters shows strong dependence on the support shape: the lattice oxygen in CeO₂ rods is more reactive than in the cubes and thus make rods a superior support for Au nanoclusters in catalyzing low temperature CO oxidation.     

类水滑石Mg3Al1-xFex负载Ir催化剂在肉桂醛加氢反应中的应用:Fe的促进作用

水滑石类化合物 (LDH) 的层板金属阳离子组成具有可调变性,通过将具有变价特性的过渡金属定量引入 LDH 层板,经热处理后可以得到具有高比表面积和层板金属原子级分散的混合金属氧化物,后者可广泛用作催化剂载体.如三元Mg-Al-Fe 类水滑石材料在光催化、H2S 选择性氧化和乙苯脱氢等反应中表现出较好的活性.Ir 催化剂在α,β-不饱和醛加氢反应中具有较好的活性,Fe 修饰 Ir 催化剂可提高不饱和醇选择性,但有关 Fe 的作用以及 Fe 与活性组分 Ir 间的相互作用本质还不是很清楚.本文以类水滑石材料 Mg3Al1–xFex为载体,采用等体积浸渍法制备了 Ir 催化剂,并用于肉桂醛加氢反应,通过考察 Fe的加入对 Ir 电子和几何结构的影响揭示了 Fe 的加入对活性和选择性的影响规律.结果表明,当 x 从 0 (Ir/Mg3Al) 增加到 1 (Ir/Mg3Fe) 时,肉桂醛加氢的反应速率在 x = 0.25 时达到最大值,肉桂醇选择性从 44.9% 增加到 80.3%,且不随肉桂醛转化率的增加而改变.透射电镜结果表明,Ir 纳米粒子的粒径随着 x 的增加未发生明显变化,均为 1.7±0.2 nm.H2程序升温还原结果发现 Ir 可以促进 Fe3+的还原且两者之间存在相互作用.X射线光电子能谱结果表明,Fe 的掺杂没有改变催化剂表面Ir0和 Ir4+含量的比值,但当 Fe 含量增加时,Fe2+2p3/2向高结合能方向偏移,且 Ir04f7/2向低结合能方向偏移,说明电子从 Fe2+转移到 Ir,形成了富电子的 Ir 物种和缺电子的 Fe 物种.富电子的 Ir 物种有利于肉桂醛分子中的 C=O 键在其表面吸附,并且和 Ir 相邻的 Fen+物种可以作为亲电位点吸附肉桂醛分子中氧,从而极化和活化 C=O 键,因而催化剂活性和选择性增大.采用吸附 CO 红外光谱表征了催化剂表面的几何结构,2058–2069 cm-1处出现了 CO 吸附峰,归属于 Ir0表面 CO 的线性吸附,高波数 2069 cm-1的吸附峰归属于 CO 在高配位 Ir 位点 (平台) 的吸附,低波数 2058 cm-1的吸附峰归属于 CO 在低配位Ir 位点 (台阶、角、楞) 的吸附.随着 Fe 含量的增加,CO 吸附峰蓝移 11 cm-1,表明 Fe 的加入改变了催化剂表面 Ir 的几何结构,低配位 Ir 位点减少,高配位 Ir 位点增多.高配位 Ir 位点 (平台) 有利于肉桂醛分子中 C=O 键的吸附,从而提高了肉桂醇的选择性.总之,Fe 的加入虽然没有明显改变 Ir 纳米粒子的粒径,但却改变了其电子和几何结构,从而提高了催化剂活性和选择性.     

Local empirical processes near boundaries of convex bodies

We investigate the behaviour of Poisson point processes in the neighbourhood of the boundary ∂K of a convex body K in ,d ≥ 2. Making use of the geometry of K, we show various limit results as the intensity of the Poisson process increases and the neighbourhood shrinks to ∂K. As we shall see, the limit processes live on a cylinder generated by the normal bundle of K and have intensity measures expressed in terms of the support measures of K. We apply our limit results to a spatial version of the classical change-point problem, in which random point patterns are considered which have different distributions inside and outside a fixed, but unknown convex body K.