双向准Z源逆变器驱动永磁同步电机的快速有限集模型预测控制

双向准Z源逆变器驱动永磁同步电机(PMSM)系统在兼具Z源逆变器和PMSM优点的同时,还可实现能量的双向流动。针对双向准Z源逆变器驱动PMSM系统的特点,提出一种快速矢量选择有限集模型预测电流控制(FCS\|MPCC)策略。由双向准Z源网络直流链电压闭环与PMSM电磁功率前馈生成电感电流参考值,由电机转速闭环生成电机电流参考值。通过预测直通(ST)及非直通(NST)状态下的电感电流值并引入电感电流子代价函数以确定是否选择ST状态,进而实现对直流链电压的控制。在NST状态下,结合空间电压矢量脉宽调制策略,仅使目标电压矢量所在扇区的4个矢量参与PMSM定子电流预测和代价函数计算,以选择最优的开关状态,在实现对PMSM转速控制的同时减小在线计算量。仿真结果表明,所提控制策略可实现对双向准Z源逆变器的升压及PMSM牵引或制动工况下的转速控制,系统具有良好的稳态及动态性能。     

电力配电网单相接地故障选线技术的发展

作为供电系统中的重要部分,电力配电网的重要性毋庸置疑。从实际情况来看单相接地故障是配电网建设与使用过程中经常出现的问题,要求专业人员提高对选线环节的重视,选择应用多种科学的选线方法以从根本上降低单相接地故障的发生风险。本文阐述了常见的几种故障选线方法,并就每一种线型的特性进行了分析。     

磷酸浓缩氟吸收问题的探讨

将我国现有的几种磷酸浓缩流程和实际应用效果进行比较 ,并从氟吸收机理分析探讨氟吸收级数与吸收效率的关系 ;氟硅酸浓度、温度与吸收效率的关系 ;吸收设备和雾沫分离器选型等     

Tasting green leaf volatiles by larvae and adults of Colorado potato beetle,Leptinotarsa decemlineata

Larvae and adults of the Colorado potato beetle,Leptinotarsa decemlineata (Say), are shown to have galeal gustatory cells that are highly sensitive to distillate of potato leaf extracts, (E)-2-hexen-1-ol, (E)-2-hexenal, and other saturated and unsaturated six-carbon alcohols. In larvae and adults, the sensory response patterns elicited by leaf homogenate, leaf distillate and a mixture of these two extracts differ in subtle ways. Beetle larvae feed most readily on Millipore disks treated with leaf homogenate and the mixture, but they did not feed on disks treated with leaf distillate. The differences in behavioral response and sensory input are used to derive a potential gustatory code that may stimulate different levels of feeding. This code may be disrupted by compounds present in nonhost leaves, thus leading to reduced feeding. Possible interactions of sapid leaf volatiles, amino acids, sugars, and potentially deterrent plant compounds are discussed.     

PRO/Ⅱ软件在硫磺回收中的二次开发应用

硫磺回收装置的设备需要有较大的操作弹性。针对其过程气进口压力低，手工计算难于进行严格的全程压降计算，现以PRO／Ⅱ软件为开发平台，通过扩充数据库和建立模拟流程，成功地将其用于硫磺回收装置的设备选型和核算，为工程设计提供了先进的计算手段。     

Maximum principle for mean-field jump–diffusion stochastic delay differential equations and its application to finance

This paper investigates a stochastic optimal control problem with delay and of mean-field type, where the controlled state process is governed by a mean-field jump–diffusion stochastic delay differential equation. Two sufficient maximum principles and one necessary maximum principle are established for the underlying system. As an application, a bicriteria mean–variance portfolio selection problem with delay is studied to demonstrate the effectiveness and potential of the proposed techniques. Under certain conditions, explicit expressions are provided for the efficient portfolio and the efficient frontier, which are as elegant as those in the classical mean–variance problem without delays.     

Efficient data selection for speech recognition based on prior confidence estimation using speech and monophone models

This paper proposes an efficient speech data selection technique that can identify those data that will be well recognized. Conventional confidence measure techniques can also identify well-recognized speech data. However, those techniques require a lot of computation time for speech recognition processing to estimate confidence scores. Speech data with low confidence should not go through the time-consuming recognition process since they will yield erroneous spoken documents that will eventually be rejected. The proposed technique can select the speech data that will be acceptable for speech recognition applications. It rapidly selects speech data with high prior confidence based on acoustic likelihood values and using only speech and monophone models. Experiments show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while providing equivalent data selection performance for speech recognition and spoken document retrieval.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

Text classification using genetic algorithm oriented latent semantic features

In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.