3篇
您的检索式：作者名="Paulo Smith Schneider"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A community-derived classification for extant lycophytes and ferns显示文摘发展史长通知了蕨类植物分类。当我们推断进化的树的能力改善了,针对认出生来的组的分类变得逐渐地预兆、稳定。这里,我们为 lycophytes 和蕨纲植物提供一个现代、全面分类,在下面,利用一条基于社区的途径类水平。我们 monophyly 用作主要标准让 taxa,而且目的识别保存两个广泛地被接受的存在 taxa 和界限并且与我们蕨类植物发展史的理解一致。总共,这个分类对待一在 337 个类, 51 个家庭, 14 目,和二个班估计了 11 916 种类。这个分类没在 lycophyte 和蕨纲植物上作为最后的词被打算分类,而是当前的假设的概括陈述,源于最好的可得到的数据并且在问题由熟悉植物的那些大多数塑造了。我们希望它将在蕨类植物上为最近的文学的那些想要的参考用作一个资源发展史和分类,为指导未来调查的一个框架,和推进讲话的刺激。 | Eric Schuettpelz Harald Schneider Alan R. Smith Peter Hovenkamp Jefferson Prado Germinal Rouhan Alexandre Salino Michael Sundue Thafs Elias Almeida Barbara Parris Emily B. Sessa Ashley R. Field Andre Luis de Gasper Carl J. Rothfels Michael D. Windham Marcus Lehnert Benjamin Dauphin Atsushi Ebihara Samuli Lehtonen Pedro Bond Schwartsburd Jordan Metzgar Li-Bing Zhang Li-Yaung Kuo Patrick J. Brownsey Masahiro Kato Marcelo Daniel Arana Francine C. Assis Michael S. Barker David S. Barrington Ho-Ming Chang Yi-Han Chang Yi-Shan Chao Cheng-Wei Chen De-Kui Chen Wen-Liang Chiou Vinicius Antonio de Oliveira Dittrich Yi-Fan Duan Jean-Yves Dubuisson Donald R. Farrar Susan Fawcett Jose Maria Gabriel y Galan Luiz Armando de Araujo Goes-Neto Jason R. Grant Amanda L. Grusz Christopher Haufler Warren Hauk Hai He Sabine Hennequin Regina Yoshie Hirai Layne Huiet Michael Kessler Petra Korall Paulo H. Labiak Anders Larsson Blanca Leen Chun-Xiang Li Fay-Wei Li Melanie Link-Perez Hong-Mei Liu Ngan Thi Lu Esteban I. Meza-Torres Xin-Yuan Miao Robbin Moran Claudine Massi Mynssens Nathalie Nagalingum Benjamin Ollgaard Alison M. Paul Jovani B. de S. Pereira Leon R. Perrie Monica Ponce Tom A. Ranker Christian Schulz Wataru Shinohara Alexander Shmakov Erin M. Sigel Filipe Soares de Souza Lana da Silva Sylvestre Weston Testo Luz Amparo Triana-Moreno Chie Tsutsumi Hanna Tuomisto IvAn A. Valdespino Alejandra Vasco Raquel Stauffer Viveros Alan Weakley Ran Wei Stina Weststrand Paul G. Wolf George Yatskievych Xiao-Gang Xu Yue-Hong Yan Liang Zhang Xian-Chun Zhang Xin-Mao Zhou | 2016 | Journal of Systematics and Evolution2016，54，6： | 44 |
| 2 | Increasing power plant efficiency with clustering methods and Variable Importance Index assessment显示文摘Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%. | Jéssica Duarte Lara Werncke Vieira Augusto Delavald Marques Paulo Smith Schneider Guilherme Pumi Taiane Schaedler Prass | 2021 | Energy and AI2021，5，3： | 0 |
| 3 | Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach显示文摘The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific restrictions.Steam generators are complex equipment and their proper opera-tion relies on the identification of their most relevant parameters.An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented.An Artificial Neural Network-ANN delivers a model to estimate the steam generator efficiency,electric power generation and flue gas outlet temperature as a function of seven input parameters.The ANN is trained with a two-year long database,with training errors of 0.2015 and 0.2741(mean absolute and square error)and validation errors of 0.32%and 2.350(mean percent and square error).That ANN model is explored by means of a combination of situations proposed by a Design of Experiment-DoE approach.All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation,while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant.DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency,electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature.The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation. | Lara Werncke Vieira Augusto Delavald Marques Paulo Smith Schneider Antônio Joseda Silva Neto Felipe Antonio Chegury Viana Madhat Abdel-jawad Julian David Hunt Julio Cezar Mairesse Siluk | 2021 | Energy and AI2021，3，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员