基于壳聚糖包埋二氧化硅-亚甲基蓝纳米复合物的电流型癌胚抗原免疫传感器的研究

合成了一种新型的SiO2-亚甲基蓝(SiO2-MB)纳米复合物.SiO2-MB纳米复合物具有不同于一般的纳米SiO2的性质,它能将MB的电子转移到电极表面,而且更重要的是减小了MB的渗漏.SiO-MB纳米复合物作为免疫传感器的媒介体使用,并采用壳聚糖(CS)包埋此复合物形成CS-SiO2-MB复合膜滴涂于洁净的玻碳电极(GCE)表面,然后在复合膜的表面固定纳米金(nano-Au)并吸附癌胚抗体(anti-CEA),制备出了性能良好的电流型免疫传感器.通过循环伏安考察了电极的电化学特性,并采用透射电子显微镜(TEM)对SiO2-MB纳米复合物的微观结构进行了表征.在最优条件下,该传感器在癌胚抗原(CEA)浓度为1～80 ng/mL范围内有良好的线性关系,检测下限为0.3 ng/mL(3倍信噪比).而且,该传感器制作简单,检测快速,稳定性较好.     

Robust neural network tracking controller using simultaneous perturbation stochastic approximation

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.     

Labview和Matlab混合编程方法的研究与实现

LabVIEW是一种高性能的图形化虚拟仪器编程软件,它在测试与测量、过程控制与处理、科学研究和分析等方面有广泛的应用,但是,LabVIEW在数值分析和数据运算处理等方面显得有些力不从心;通过其强大的外部接口,提出了基于DDE,DLL,ActiveX自动化,MATLAB Script节点技术的LabVIEW和MATLAB混合编程,并结合小波降噪、虚拟模糊热点温度分析仪实例,对这些方法进行了比较分析;利用混合编程既良好地发挥了仪器语言的优势,又扩展了算法工具箱,探索了智能虚拟仪器新的开发途径。     

A novel generic hebbian ordering-based fuzzy rule base reduction approach to mamdani neuro-fuzzy system

There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.     

Comments on “Adaptive variable structure set-point control ofunderactuated robots” [and reply]

This note points out several technical errors in the above mentioned paper by Su et al. (ibid. vol.44 (1999)). It is shown that the choice of Lyapunov function is inadequate for the system stability analysis, and the asymptotic tracking of the underactuated robot joints is not achievable under certain conditions. In reply, Su addresses, from the comments, some important issues that were not duly emphasised in the paper     

Discrete J-spectral factorization

This paper deals with the J-spectral factorization for general discrete rational matrices. A simple approach based on the Kalman filtering in Krein space is proposed. The main idea is to construct a stochastic state space filtering model in Krein space such that the spectral matrix of the output is equal to the rational matrix to be factorized. The spectral factor is then easily derived by using the generalized Kalman filtering in Krein space, which is similar to the H₂ spectral factorization. Our approach unifies the treatment of the H₂ spectral factorization and the J-spectral factorization. The applications of the derived results in H_∞ and risk-sensitive estimation for both nonsingular and singular systems are demonstrated.     

Deformation and failure of adhesive bonds under shear loading

Experiments have shown that certain mechanical properties can be greatly enhanced when a material is stressed while under tight spatial constraint. In this work, the post-yield behaviour of brittle and ductile epoxy resins used as thin adhesive bonds was determined using the napkin ring shear test. Real-time observations of the deformation in the bond as well as SEM post-failure analysis were employed to gain information on the failure process. The complete stress-strain histories of the adhesives were established for bond thicknesses ranging from the micrometre level up to values large enough to expose the bulk properties. The most dramatic variations occurred for the ultimate shear strain, _f; for the brittle adhesive, _f increased by over 30-fold relative to the bulk material when the bond thickness, t, was decreased to a few micrometres. Experimental evidence and analytical considerations suggest that the decline of _f with t was due to premature bond failure caused by tensile microcracks or voids that were formed in the interlayer during loading, with the specific _f versus t relationship being a mere reflection of the variations in the degree of stress concentration at the tip of the flaws. The astonishingly large value of _f (i.e. 2.8–3.4) found for the brittle epoxy in the micrometre thickness range, is believed to represent the intrinsic shear strain of this material.     

Observation of deformation and damage at the tip of cracks in adhesive bonds loaded in shear and assessment of a criterion for fracture

The evolution of damage at the tip of cracks in adhesive bonds deforming in shear was monitored in real time using a high-magnification video camera. Brittle and a ductile epoxy resins were evaluated, with the bond thickness t being an experimental variable. An extensive zone of plastic deformation developed ahead of the crack tip prior to fracture. In the case of the brittle adhesive, for relatively thick bonds tensile microcracks formed within that zone. Increased loading caused the microcracks to grow from the interlayer to the interface, which led to a complete bond separation after interface cracks emanating from adjacent microcracks linked. In contrast, for the ductile adhesive the crack always grew from the tip. Strain gradients tended to develop there when the bond thickness was large.The adhesive shear strain was determined from fine lines scratched on the specimen edge. For both adhesives, the average crack tip shear strain at crack propagation rapidly decreased with increasing t. This effect was attributed to the changing sensitivity of the bond to the presence of flaws; thicker bonds can accommodate larger microcracks or microvoids which cause greater stress concentration. For a given bond thickness, the critical crack tip shear strain agreed well with the ultimate shear strain of the unflawed adhesive previously determined using the napkin ring shear test [12]. This suggests that the ultimate shear strain is a key material property controlling crack growth. The critical distortional strain energy/unit area of the unflawed adhesive W _s was determined from the area under the stress-strain curve in the napkin ring test. Good agreement between W _s and the adhesive mode II fracture energy was found for all joints tested except for relatively thick bonds. For the particular case of an elastic-perfectly plastic adhesive, the agreement above implies % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGak0dh9WrFfpC0xh9vqqj-hEeeu0xXdbba9frFj0-OqFf% ea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs0dXdbPYxe9vr0-vr% 0-vqpWqaaeaabaGaciaacaqabeaadaqaaqaaaOqaaGqaciaa-Deada% WgaaWcbaacbaGaa4xsaiaa+LeacaGFdbaabeaakiabg2da9iaa-Dfa% daWgaaWcbaGaa83CaaqabaGccqGHHjIUcaWF0bGaeqiXdq3aaSbaaS% qaaiaa-LhaaeqaaOGaeq4SdC2aaSbaaSqaaiaa-zgaaeqaaaaa!463A!\[G_{IIC} = W_s \equiv t\tau _y \gamma _f \].     

Improved MCMAC with momentum, neighborhood, and averagedtrapezoidal output

An improved modified cerebellar articulation controller (MCMAC) neural control algorithm with better learning and recall processes using momentum, neighborhood learning, and averaged trapezoidal output, is proposed in this paper. The learning and recall processes of MCMAC are investigated using the characteristic surface of MCMAC and the control action exerted in controlling a continuously variable transmission (CVT). Extensive experimental results demonstrate a significant improvement with reduced training time and an extended range of trained MCMAC cells. The improvement in recall process using the averaged trapezoidal output (MCMAC-ATO) are contrasted against the original MCMAC using the square of the Pearson product moment correlation coefficient. Experimental results show that the new recall process has significantly reduced the fluctuations in the control action of the MCMAC and addressed partially the problem associated with the resolution of the MCMAC memory array.     

基于数字电路系统的高速PCB信号完整性分析

信号缺失、信号串扰等问题要求电路板设计初期考虑信号完整性.为此,基于高速通用信号处理平台在设计中所遇到的信号完整性的阻抗匹配问题进行探讨,并对阻抗匹配进行仿真,通过调整端接匹配电阻,得出优化结果.结果表明,实验结果与实际情况相符合.