视网膜光学反差法治疗少儿弱视的体会

从1987年7月至1988年8月,我们采用辽宁省本溪射线仪器厂生产的视网膜光学反射镜(JGY—Ⅰ型近视眼光学治疗仪)对61例89只少儿弱视眼进行临床疗效观察。眼没有器质性改变,而矫正视力不能达到正常者称为弱视。弱视发病机理分为:斜视性弱视、屈光参差性弱视、形觉丧失性弱视、先天性弱视和屈光不正性弱视。患者全部为门诊确诊治疗的少年儿童(年龄6～17岁)。男性24例、女性37例。其中单眼弱视33例,男性14例,女性19例。右眼弱视者13人,左眼弱视者20人,共61例,89只眼。

韩愈文心与其诗章法

本文以韩愈文心为透镜，以宋人因韩为参照，着重对韩愈诗章法进行由诗人到作品、由文本到接受的立体审视，认为韩愈诗章法体现了其创新精神和情感表现意识，而宋人沿其途、扬其波，从而揭示出韩愈诗在唐之间承前启后的重要地位。

新型高速纱线毛羽仪的设计与实现

针对高速毛羽仪国产化的需求,设计并实现一种基于图像识别的高速纱线毛羽仪。设计使用了大功率840 nm10串10并红外LED阵列光源搭光源驱动器、背照式CCD照相机对图像进行采集。对采集到的图像双边滤波去噪,改进动态阈值反差法进行阈值分割和二值化,采用最大区域提取法去除孤立点,通过腐蚀膨胀形态差分学得到纱干和毛羽图像,提高了图像识别处理和计数速度,可以实现400 m/min高速下对纱线毛羽的精确检测。与USTER ZWEIGLE HL400型毛羽仪相比,纱线在400 m/min检测条件下,高速纱线毛羽仪所测毛羽数相对偏差率6.2%,准确性高,稳定性强。

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Controlling Chaos in Hénon Map by the Constant Feedback Method

We demonstrate the constant feedback and the modified constant feedback method to the Hénon map. Using the convergence of the chaotic orbit in finite time, we can control the system from chaos to the stable fixed point, and even to the stable period-2 orbit or higher periodic orbit by the action of a proper feedback strength and pulse interval. We also find that the multi-steady solutions appear with the same control strength and different initial conditions. The aim of this control method is explicit and the feedback strength is easy to determine. The method is robust under the presence of weak external noise.

Developing energy forecasting model using hybrid artificial intelligence method

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution （DE） algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the con- vergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model oarameters for a comolex mathematical model.

Non-isothermal decomposition kinetics of hydrogarnet in sodium carbonate solution

Carbonation decomposition of hydrogarnet is a significant reaction of the calcification-carbonation new method for alumina production by using low-grade bauxite.In this work,non-isothermal decomposition kinetics of hydrogarnet in sodium carbonate solution was studied by high-pressure differential scanning calorimetry(HPDSC) at different heating rates of 2,5,8,10,15 and 20 K·min^(-1),respectively.The activation energy(E_α) was calculated with the help of isoconversional method(model-free),and the reaction mechanism was determined by the differential equation method.The calculated activation energy of this reaction was 115.66 kJ·mol^(-1).Furthermore,the mechanism for decomposition reaction is Avrami-Erofeev(n=1.5),and the decomposition process is diffusion-controlled.

A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

Modeling bidirectional reflection distribution function of microscale random rough surfaces

The radiative properties of three different materials surfaces with one-dimensional microscale random roughness were obtained with the finite difference time domain method(FDTD) and near-to-far-field transformation.The surface height conforms to the Gaussian probability density function distribution.Various computational modeling issues that affect the accuracy of the predicted properties were discussed.The results show that,for perfect electric conductor(PEC) surfaces,as the surface roughness increases,the magnitude of the spike reduces and eventually the spike disappears,and also as the ratio of root mean square roughness to the surface correlation distance increases,the retroreflection becomes evident.The predicted values of FDTD solutions are in good agreement with the ray tracing and integral equation solutions.The overall trend of bidirectional reflection distribution function(BRDF) of PEC surfaces and silicon surfaces is the same,but the silicon's is much less than the former's.The BRDF difference from two polarization modes for the gold surfaces is little for smaller wavelength,but it is much larger for the longer wavelength and the FDTD simulation results agree well with the measured data.In terms of PEC surfaces,as the incident angle increases,the reflectivity becomes more specular.

Effect of selected signals of interest on ultrasonic backscattering measurement in cancellous bones

This study examined how the signals of interest （SOI） effect on the backscattering measurement numerically based on 3-D finite-difference time-domain （FDTD） method. High resolution microstructure mappings of bovine cancellous bones provided by micro-CT were used as the input geometry for simulations. Backscatter coefficient （BSC）, integrated backscatter coefficient （IBC） and apparent integrated backscatter （AIB） were calculated with changing the start （L1） and duration （L2） of the SOl. The results demonstrated that BSC and IBC decrease as L1 increases, and AIB decreases more rapidly as L1 increases. The backscattering parameters increase with fluctuations as a function of L2 when L2 is less than 6 mm. However, BSC and IBC change little as L2 continues to increase, while AIB slowly decreases as L2 continues to increase. The results showed how the selections of the SOI effect on the backscattering measurement. An explicit standard for SOl selection was proposed in this study and short L1 （about 1.5 mm） and appropriate L2 （6 mm-12 mm） were recommended for the calculations of backscattering parameters.

Kinetics for hydrogen production by methanol steam reforming in fluidized bed reactor

Hydrogen is one of the best energy carriers.Fluidized bed reactor provides a promising approach for hydrogen production. To describe the hydrogen generating rate with methanol steam reforming in fluidized bed reactor quantitatively, dual-rate kinetic models of the reactions with exponent form were developed, including that of steam reforming reaction(SR) and decomposition reaction(DE).The reaction rate per unit mass of catalyst was related to partial pressures of components. The exponentials in kinetic equations were obtained by linear least-squares method based on the experimental data. The variance homogeneity test(F test) shows that the dynamic models are feasible with high accuracy, which can be used to predict the generating rate of hydrogen under different reaction temperatures and feed flow rates in fluidized bed reactor. The SR and DE activation energy obtained indicates that ESR\ EDE, which can explain the previous observation that the CO_2 selectivity decreased with the temperature increase.