Enhancement of power system data debugging using GSA-based data-mining technique

In this paper, a gap-statistic-algorithm (GSA)-based data-mining technique is applied to enhance the data debugging in power system operations. In the proposed approach, the GSA technique is embedded into a neural network frame in anticipation of improving the detection capability of bad data. Thanks to the clustering capability exhibited by GSA in which the number of clusters can be optimally determined, the proposed approach becomes highly effective to localize the group of abnormal data. This proposed approach has been tested through the data collected from different scenarios made on an IEEE 30-bus system and 118-bus systems. Test results reveal the feasibility of the method for the data diagnosis applications.     

Characterization of ultrashort optical pulses with third-harmonic-generation based triple autocorrelation

We present a method to obtain complete information of femtosecond pulses. By measuring triple-optical autocorrelation directly with third-harmonic generation, without spectral information, a temporal pulse shape can be obtained by analytical calculation without direction-of-time ambiguity. Combining the resulting optical pulse shape with its corresponding optical spectrum, the exact phase and color variations in time can all be recovered with a Gerchberg-Saxton algorithm through an iterative calculation with an O(n) complexity.     

等规聚丙烯β晶型成核剂研究

精选了一系列有机和无机化合物为成核剂,使用砂浴法和热台法制备了一系列不同β晶含量的等规聚丙烯,发现最有效的β成核剂是溶靛素灰 IBL 和溶蒽素金黄 IGK,首次表明β成核剂具有排列紧密的稠球结构特征,且含有硫原子。     

石蜡光氯化合成氯蜡－70的研究──（Ⅲ）反应过程致色因素考察及生色机理研究

氯蜡－７０是—应用广泛、性能优良的精细化工产品，其色泽是—重要的技术指标，直接影响着产品的质量和应用。本文考察了合成反应过程导致生色的因素，指出原料中含有的少量环烷烃、芳烃、杂环有机物及少量无机杂质能影响产品的色泽，实验表明温度过高，光源波长选择不当也是致色的重要原因。研究了致色的过程，提出其生色历程是按照β－消除反应机理进行的，并做了具体描述。     

管外上升R－113流体的沸腾换热试验

详细介绍了Ｒ－１１３流体管外上升流动的沸腾换热试验，包括试验台架、流量计和温度测量的修正，以及试验数据。拟合的换热关系式为ｑ＝５２８（Ｔｗ─Ｔｓ）１．３３。     

淀积条件对GDa—SiNx薄膜电学特性的影响

本文讨论了用辉光放电法制备氮化硅薄膜时衬底温度、射频功率和气体流量比对薄膜的电导率、介电常数和击穿强度的影响。通过优化生长条件，制备了优质非晶氮化硅薄膜，其介电常数为７．５、击穿强度为５．５ＭＶ／ｃｍ、电导率为１０－１３（Ωｃｍ）－１。     

Two-level optimal output feedback stabilizer design

The paper addresses the design of two-level power system stabilizers using an optimal reduced order model whose state variables are torque angles and speeds. The reduced order model retains their physical meaning and is used to design a two-level linear feedback controller that takes into account the realities and constraints of electrical power systems. The two-level control strategy is used, and a global control signal is generated from the output variables to minimize the effect of interactions. The effectiveness of this controller is evaluated and a multimachine system is given as an example to illustrate the advantages of the proposed method. Responses of the system with a two-level scheme and an optimal reduced order scheme are included for comparative analysis.     

Gate and drain currents in off-state buried-type p-channel LDDMOSFETs

The buried-type p-channel LDD MOSFETs biased at high positive gate voltage exhibit novel characteristics: (1) the ratio of the drain to gate currents is about 1×10^-3 to 5×10^-3; and (2) the gate and drain currents both are functions of only the gate voltage minus the n-well bias. Such characteristics are addressed based on the formation of the surface n ⁺ inversion layer due to the punchthrough of the buried channel to the underlying shallow p-n junction. The measured gate current is due to the Fowler-Nordheim tunneling of electrons from this inversion layer surface and the holes generated within the high-field oxide constitute the drain current. The n⁺ inversion layer surface potential is found to be equal to the n-well bias plus 0.55 V. As a result, both the oxide field and the gate and drain currents are independent of drain voltage     

On-line learning delivery decision support system for highlyproduct mixed semiconductor foundry

A production learning system (PLS) based on the tool model was constructed as a decision support and real-time information update system to forecast the cycle time. A tool model includes a waiting model and a processing model. Each of the waiting and processing models uses a backpropagation neural network to establish the relationship between the input and output (time) of the model. Hence, cycle time estimation, tool group move and confirm line item performance (CLIP) value can be obtained based on the memory stored in the neural network. The result shows that the forecasting ability of the PLS has an error rate below 8% on average