A cooperative Bayesian and lower bound estimation in dynamic framed slotted ALOHA algorithm for RFID systems

A novel estimation scheme that combines Bayesian and lower bound estimating radio frequency identification tag population size is proposed. The developed methodology is based on the fusion between the Bayesian and lower bound estimating techniques. It turns out that the fusion rule is built up thanks to an existing linear relationship between the cited techniques. Simulation results show that the developed technique significantly improves the accuracy of the estimating tag quantity and presents less estimation error. Also, the resulting advanced dynamic framed slotted ALOHA protocol considerably improves the performance and efficiency of the radio frequency identification anti‐collision compared with the most recent protocols using others estimating methods.     

钢-混凝土粘结预测的人工神经网络模型

提出人工神经网络模型来模拟传统的带肋钢筋和混凝土之间的粘结性能,目的是预测钢筋从混凝土混合物中拔出的极限荷载(第一神经网络模型)或抗压强度(第二神经网络)以及根据RILEM试验设计的不同钢筋直径的拔出极限荷载。采用112个带肋钢筋(直径为10mm、12mm)以及三种不同混凝土配合比的拔出试验结果数据库,对神经网络模型进行训练。根据反向传播算法,进行多层感知器训练。第一个模型(ANN-6)有6个输入:钢筋直径、水灰比、砂石比、级配、水泥种类和混凝土龄期。第二个模型(ANN-2)有2个输入:钢筋直径、混凝土抗压强度,两个模型的输出均为极限拔出荷载。研究结果显示:所采用的模型预测精度高、误差低、具有鲁棒性。从鲁棒性方面,第一个模型(ANN-6)比第二个模型(ANN-2)更精确。将混凝土的成分作为输入参数,而不是混凝土的强度,对于带肋钢筋-混凝土界面的局部现象更具代表性。     

燃煤电厂玻璃钢内筒套筒式烟囱设计

为达到环保要求和节省成本,燃煤电厂常采用湿法脱硫不上GGH工艺.文章基于作者设计的国内首座大型玻璃钢内筒套筒式烟囱(180/Φ6.6 m),从结构总体布置、计算模型简化与计算、FRP内筒结构设计、铺层与材料设计、试验验证、连接构造等方面系统的阐述了玻璃钢烟囱的设计方法,并对需要注意的问题进行了重点说明.可供有关工程技术人员参考.     

Artificial neural network model for steel–concrete bond prediction

In this paper, an Artificial Neural Network (ANN) is proposed for modelling the bond between conventional ribbed steel bars and concrete. The purpose is to predict the ultimate pull-out load from the concrete mix constituents (first ANN model) or the compressive strength (second ANN model) and from the steel bar diameter according to the RILEM test configuration [RILEM. Essai portant sur l’adhérence des armatures du béton: essai par traction. Materials and Structures 1970; 3 (3) 175–78]. The ANN models were implemented using an experimental database of 112 pull-out test results performed with ribbed bars 10 mm or 12 mm in diameter and three concrete mixes with different constituent proportions. A Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The first model has six inputs (ANN-6): the diameter of the ribbed bar, the water to cement ratio, the gravel to sand ratio, the crushed to rolled gravel ratio, the type of cement and the concrete maturity. The second model has two inputs (ANN-2): the diameter of the bar and the concrete compressive strength. The ultimate pull-out load was the output data for both models.The results show that the implemented models have good prediction and generalisation capacity with low errors. The ANN-6 model is more accurate, regarding the generalisation capacity, than the ANN-2 model. Concrete mix constituents as input parameter, instead of the compressive strength, are more representative of the local phenomenon at the steel-ribs-to-concrete interface.     

Advanced metaheuristic optimization techniques in applications of deep neural networks: a review

Neural Computing and Applications - Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a...     

用UV—VIS法鉴别食品级玻璃纲质量的研究

本文用紫外—可见光分光光度法(UV—VIS法)研究了不饱和聚酯树脂(UP树脂)固化物中残存小分子的种类,以及用该法测定这些小分子含量时的最佳萃取溶剂及最佳萃取时间。并用混合溶液的吸光度具有加和性的原理,测定了几种UP树脂固化物中小分子的含量。指出将UV—VIS法用于食品级聚,酯玻璃钢质量检测,更适合我国国情。     

Influence of anode roughness and buffer layer nature on organic solar cells performance

Fluorine doped transparent conductive tin oxide thin films (FTO) of different surface roughness have been deposited by chemical vapor deposition (FTO_SOL), classical chemical spray pyrolysis (FTO_CSP), and spray pyrolysis onto heated substrates using infra red irradiation (FTO_IRSP); the three deposition methods inducing different surface roughness. It was found that the different FTOs presented similar electrical properties while their structural, morphological and optical properties were related to surface properties. These FTO films have been used as anode in multilayer organic solar cells, based on coupled donor/acceptor-copper phthalocyanine/fullerene. To improve solar cell performance, buffer layers of different natures have been tried at the anode/organic material interface. Deposition of a thin molybdenum oxide film onto FTO smooth films afforded reproducible devices with performance similar to those obtained with indium tin oxide anodes. However, cell efficiency decreased as FTO surface roughness increased. The degree of degradation depended on the nature of the buffer layer. We show that it is necessary to use buffer layer material that allows consistency and completeness of the electrode coverage.