Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

铁矿石境外投资风险评估的模糊神经网络模型

论文将模糊理论与神经网络结合,对铁矿石境外投资所涉及的风险因素分别从资产影响、威胁频度两方面进行分析,建立了铁矿石境外投资的风险层次化结构,并构造了各因素所对应评判集的隶属度矩阵;综合运用模糊推理算法与神经网络仿真技术,对铁矿石境外投资的风险进行评估,进而判定铁矿石境外投资风险等级。最后,通过计算铁矿石境外投资的市场风险说明了算法的应用,并将结果与模糊综合评估得到的风险值进行比较,检测了模型的有效性。     

高斯颜色模型在瓷片图像分类中的应用

由于RGB颜色空间不能很好贴近人的视觉感知,同时也缺少对空间结构的描述,因此采用兼顾颜色信息和空间信息的高斯颜色模型以获取更全面的特征,提出了一种基于高斯颜色模型和多尺度滤波器组的彩色纹理图像分类法,用于瓷器碎片图像的分类。首先将原始图像的RGB颜色空间转换到高斯颜色模型;再用正规化多尺度LM滤波器组对高斯颜色模型的3个通道构造滤波图像,并借助主成分分析寻找主特征图,接着选取各通道的最大高斯拉普拉斯和最大高斯响应图像,与特征图联合构成特征图像组用以进行参数提取;最后以支持向量机作为分类器进行学习和分类。实验结果表明,与基于灰度的、基于RGB模型的和基于RGB_bior 4.4小波的方法相比,本文方法具有更好的分类结果,其中在Outex纹理图像库上获得的分类准确率为96.7%,在瓷片图像集上获得的分类准确率为94.2%。此方法可推广应用到其他彩色纹理分类任务。     

红外密码锁设计

为了改进现有的开锁设备,设计了一种红外密码锁,分为红外密码发送端和红外信号接收端两部分,分别以8位单片机STC89C52为核心进行控制。在发送端以红外发射二极管发射的红外光为载波,进行密码信号的发射与传输,该密码由使用者从矩阵键盘输入。在接收端进行红外密码信号的接收、处理和验证,并作出开锁或报警响应。该系统结构简单、成本低、实用性强可适用于智能家居仓储管理等场合.     

热喷焊技术在导板强化中的应用

用氧化乙炔焰热喷焊技术对普碳钢导卫装置进行表面强化，制成复合材导卫以替代高碳高合金导卫，取得较好效果。     

Fabrication of Nano-TiO2 Coating for Dye-Sensitized Solar Cell by Vacuum Cold Spraying at Room Temperature

Deposition of nanocrystalline TiO₂ coating at low temperature is becoming more attractive due to the possibility for continuous roll production of the coating for assembly lines of dye-sensitized solar cell (DSC) at a low cost. In this study, porous nano-TiO₂ coating was deposited by vacuum cold spraying (VCS) at room temperature on a conducting glass substrate using commercial P25 nanocrystalline TiO₂ powder. The microstructure of TiO₂ coating was characterized by field emission scanning electron microscopy (FESEM) and nitrogen adsorption test. A commercial dye (N719) was adsorbed on the surface of TiO₂ particles within the coating to assemble a DSC. The cell performance was evaluated by employing simulated solar light at an intensity of 100 mW/cm². The results showed that TiO₂ coating was deposited by the agglomerates of nano-TiO₂ powders. The Brunauer-Emmett-Teller (BET) test of the as-sprayed TiO₂ coating yielded a porosity of 49% and an average pore size of 17 nm. The assembled solar cell yielded a short-circuit current density of 7.3 mA/cm² and an energy conversion efficiency of 2.4%. The test results indicate that VCS was a promising method to deposit nanocrystalline TiO₂ coatings at low temperature applied to DSCs. This article is an invited paper selected from presentations at the 2007 International Thermal Spray Conference and has been expanded from the original presentation. It is simultaneously published in Global Coating Solutions, Proceedings of the 2007 International Thermal Spray Conference, Beijing, China, May 14-16, 2007, Basil R. Marple, Margaret M. Hyland, Yuk-Chiu Lau, Chang-Jiu Li, Rogerio S. Lima, and Ghislain Montavon, Ed., ASM International, Materials Park, OH, 2007.     

巨磁电阻材料的研究与应用

介绍了一种新型的磁性功能材料－巨磁电阻材料，综述了其研究进展情况。并就它在巨磁电阻传感器、高密度磁记录读出磁头、巨磁电阻随机存储器以及自旋晶体管等磁电子元器件上的进行了论述。     

A power fluctuation evaluation method of PV plants based on RankBoost ranking

Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.     

Damping forced oscillations in power system via interline power flow controller with additional repetitive control

With the continuous expansion of power systems and the application of power electronic equipment, forced oscillation has become one of the key problems in terms of system safety and stability. In this paper, an interline power flow controller (IPFC) is used as a power suppression carrier and its mechanism is analyzed using the linearized state-space method to improve the system damping ratio. It is shown that although the IPFC can suppress forced oscillation with well-designed parameters, its capability of improving the system damping ratio is limited. Thus, combined with the repetitive control method, an additional repetitive controller (ARC) is proposed to further dampen the forced power oscillation. The ARC control scheme is characterized by outstanding tracking performance to a system steady reference value, and the main IPFC controller with the ARC can provide higher damping, and further reduce the amplitude of oscillations to zero compared with a supplementary damping controller (SDC). Simulation results show that the IPFC with an ARC can not only greatly reduce the oscillation amplitude, but also actively output the compensation power according to the reference value of the ARC tracking system.     

Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification

To improve the accuracy of cross-modal pedestrian re-identification,a reciprocal bi-directional generative adversarial network-based method is proposed.First,we build two generative adversarial networks to generate cross-modal heterogeneous images.Second,an associated loss is designed to pull close the distribution of features in latent space during the image translation between visible and infrared images so as to help the networks generate fake heterogeneous images that have high similarity with the real images.Finally,by concatenating the original and generated heterogeneous pedestrian images into the discriminative feature extraction network,images from different modalities can be unified into a common modality,thus suppressing the cross-modal gap.Representation learning and metric learning are utilized to achieve more discriminative pedestrian features.Comparative experiments are conducted on SYSU-MM01 and RegDB datasets to analyze the accuracy with different loss functions.Compared with other state-of-the-art cross-modal pedestrian re-identification methods,the proposed method achieves a higher accuracy and stronger robustness.