水轮机调节系统的状态PI控制器研究

虽然状态PI控制器已经提出很多年,但至今未在水轮机转速控制中有所应用。本文应用状态PI控制,从水轮机控制系统状态反馈模型出发,设计水轮机调节系统状态PI控制器,并对状态PI最优控制与传统推荐PID最佳调节进行比较分析。仿真结果表明:两者动态过程明显不同,前者的动态过程呈现非周期快速稳定特点,而后者的动态过程则为衰减振荡过程,并且状态PI控制在负荷变化时有更好的控制品质。     

支持状态机建模的工作流引擎研究

为了能清晰地描述存在较多审批环节和步骤跳转的业务流程,提出利用状态机为流程建模的方法。通过改进传统的关系结构工作流引擎,给出一个支持状态机建模的关系结构工作流引擎的设计方案,详细介绍该引擎的设计原理和实现柔性方法。最后通过一个示例,演示支持状态机建模的工作流引擎的工作机制和实例迁移的过程。原型已经应用到高校学费管理系统中。实践证明,该引擎可以有效地支持状态机建模,并缩短关键业务的开发周期。     

电位滴定法研究~(99)Tc-HMDP和~(99)Tc-HEDP中Tc的氧化态

~(99)Tc-HMDP和~(99)Tc-HEDP溶液是在相应的螫合剂存在下,用过量SnCl_2还原~(99)TcO_4-制得。用标准I_2液对SnCl_2进行了剩余量电位滴定,确定了在pH～3的介质中,~(99)Tc-HMDP和~(99)Tc-HE-DP中~(99)Tc的氧化态均为+4。全部实验在N_2气流中进行。~(99)Tc-HMDP和~(99)Tc-HEDP的紫外光谱表明:在用I_2滴定的过程中,~(99)Tc只处于一种氧化态+4;将它们放置在空气中,一小时后仍较稳定。     

Interpreting the negative mood-helping literature via "mega"-analysis: A contrary view.

"Mega"-analysis was developed by M. Carlson and N. Miller (see record 1987-31249-001) as an extension of traditional meta-analytic procedures for conducting integrative reviews of existing research literatures. One such mega-analysis was conducted by Carlson and Miller to synthesize the literature on the relation between negative mood states and helping. That analysis found no support for a theoretical account (negative state relief) that had been confirmed previously by using various experimental approaches. In an attempt to reconcile the discrepancy, the logic and methods used in Carlson and Miller's mega-analysis of the negative mood-helping literature were examined, and several serious problems were found. These problems are discussed, and data are presented to show that the results of that mega-analysis, and perhaps all mega-analyses, should not be viewed with confidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Third-generation CALPHAD description of pure GeO2 at 1 atm

The thermodynamic properties of crystalline and liquid/amorphous phases of germanium dioxide have been evaluated using DFT calculations and third-generation CALPHAD models. The values for the standard entropies of crystalline and amorphous phases were reassessed through use of the Planck-Einstein approach. This allowed a more physically-based representation of the set of thermodynamic properties of germanium dioxide to be obtained.     

引入关联分析的用户行为信任评估在云计算平台中的研究

关联规则是数据挖掘的重要方法。随着国内外云计算应用及研究的不断推进,云计算的安全性成为云计算的核心问题之一。通过进行用户的状态行为关联分析,提取信任信息,结合可信云的思想,提出一个云计算下基于信任的防御系统模型。     

液压支架工作状态模糊识别系统研究

针对目前综采工作面液压支架压力监测系统只能监测综采工作面压力,不能对液压支架工作状态进行识别的问题,设计了一种液压支架工作状态模糊识别系统。该系统可对现有的综采工作面液压支架压力监测系统监测到的压力数据进行模糊识别,根据模糊识别输出值即可判断液压支架的5种工作状态,即降架、移架、升架、增阻、卸压保持;当液压支架处于增阻工作状态且增阻时间过长时,该系统可及时通知液压支架操作工增大液压支架压力,辅助实现顶煤破碎。测试结果验证了该系统的可行性。     

基于强跟踪滤波器的开关变换器故障诊断

Buck变换器又称降压变换器、串联开关稳压电源、三端开关型降压稳压器。广泛用于计算机、家用电器、控制系统和通信系统中的电源几乎都是开关电源,具有超快速负载动态响应、高功率变换效率、高功率密度、低电压、大电流的特性.开关变换器作为它的主要电路,故障率较高,对它故障诊断研究是有意义的。基于强跟踪滤波理论,给出了一种开关变换器电路故障实时诊断的一种方法。该方法以建立一种开关变换器电路状态空间模型为基础,利用强跟踪滤波器对电路状态及元件参数进行估计,当元件参数发生软、硬型故障时,根据强跟踪滤波器对元件参数的跟踪结果及修正的Bayes分类算法,可实时诊断开关变换器电路中的元件故障。仿真结果证明了该方法的有效性。