大小数量加工机制分离现象的ERP研究

大量行为研究表明,个体对4以上非符号数量的加工存在加工机制分离现象,但是相关的神经机制研究还很少。本研究选取21名大学生被试,结合ERP技术和数量判断任务,以数量比率效应为指标,比较4以上不同大小数量加工的神经反应模式。结果显示,大小数量加工条件下数量比率效应对P2p成分幅值的调节模式存在显著差异。这支持了小数量加工基于数量属性,大数量加工基于非数量属性的观点。

基于小数量贴片天线的信号波达方向测试方法

传统来波信号方向估计算法存在计算量大、工程实现难度高、阵子效率低的问题。为了解决上述问题,文章提出了一种利用小数量贴片天线的来波信号二维到达角估计方法。该方法利用比幅测向技术,按照天线排布将空间划分成3个区域,在每个区域建立空间查找表,对来波信号到达角精确估计。基于以上方法,文章用Matlab对空间不同方向到达的信号进行了仿真分析,估计误差在0.5°以内,估计时间相对于MUSIC算法显著提高,验证了该方法对来波信号方向估计的准确性与时效性。该方法可以应用于便携式终端,在民用方面为信号方向估计提供一种可行的方法。

Several Classes of Two-weight or Three-weight Linear Codes and Their Applications

Recently,linear codes with a few weights have been extensively studied due to their applications in secret sharing schemes,constant composition codes,strongly regular graphs and so on.In this paper,based on the Weil sums,several classes of two-weight or three-weight linear codes are presented by choosing a proper defining set,and their weight enumerators and complete weight enumerators are determined.Furthermore,these codes are proven to be minimal.By puncturing these linear codes,two classes of two-weight projective codes are obtained,and the parameters of the corresponding strongly regular graph are given.This paper generalizes the results of[7].

视知觉线索对幼儿小数离散量表征的影响

本研究采用数量辨别任务和数量比较的干扰任务考察了元素累积面积和元素特征丰富度两种视知觉线索对3～6岁幼儿表征4以内离散量的影响。两个任务的被试分别为80名和74名。结果表明,幼儿在辨别小数离散量时并未表现出对元素累积面积的偏好;且幼儿能同时关注累积面积和饼干个数两个维度,从而辨别抽象的数量关系;这种辨别能力有随年龄提高的趋势,不能辨别组以3、4岁幼儿居多,他们辨别方形饼干的数量关系比辨别圆形饼干更容易。当元素累积面积干扰小数量比较时,幼儿数量比较的正确率随数量比例增大而降低,此时幼儿小数量表征不再精确;而当单个元素的知觉特征更鲜明时,即使仍存在元素累积面积干扰,其正确率也不再受数量比例的影响。这说明单个元素的知觉线索与整体知觉线索共同影响小数离散量表征,也提示着幼儿的小数量表征精确性可能并非稳定存在,小数量表征在特定条件下也需要借助模拟数量系统。

《祖堂集》中的助词“许”及其相关结构

《祖堂集》中的助词“许”可以处于两种不同的限定性结构,其不同在于二者语法位置前后互补。两种结构均表极小数量意义,具备行业特征。

Application of Lyapunov exponent algorithm in balise signal chaotic oscillator detection

To improve the detection accuracy of the balise uplink signal transmitted in a strong noise environment,we use chaotic oscillator to detect the balise uplink signal based on the characteristics of the chaotic system that is sensitive to initial conditions and immune to noise.Combining with the principle of Duffing oscillator system used in weak signal detection and uplink signal feature,the methods and steps of using Duffing oscillator to detect the balise signal are presented.Furthermore,the Lyapunov exponent algorithm is used to calculate the critical threshold of the Duffing oscillator detection system.Thus,the output states of the system can be quantitatively judged to achieve demodulation of the balise signal.The simulation results show that the chaotic oscillator detection method for balise signal based on Lyapunov exponent algorithm not only improves the accuracy and efficiency of threshold setting,but also ensures the reliability of balise signal detection.

Engineering properties of sintered waste sludge as lightweight aggregate in a densified concrete mixture

The global trend towards carbon reduction,energy conservation,and sustainable use of resources has led to an increased focus on the use of waste sludge in construction.We used waste sludge from a reservoir to produce high-strength sintered lightweight aggregate,and then used the densified mixture design algorithm to create high-performance concrete from the sintered aggregate with only small amounts of mixing water and cement.Ultrasonic,electrical resistance and concrete strength efficiency tests were perfo...

Changes of Soil Microbial Biomass C and P During Wheat Growth After Application of Fertilizers

A pot experiment was carried out with a clay loam in a green house. The results showed that soil microbial biomass C increased with the application of organic manure at the beginning of the experiment and then gradually decreased with declining of the temperature. The soil biomass C increased at the tillering stage when the temperature gradually increased, and rose to the highest value at the anthesis stage, being about 554.9-794.4 mg C kg-1. The application of organic manure resulted in the highest increase in biomass C among the fertilization treatments while that of ammonium sulphate gave the lowest. At the harvest time the soil biomass C decreased to the presowing level. Like the soil biomass C the amount of biomass P was increased by the incorporation of organic manure and was the highest among the treatments, with the values of the check and ammonium sulphate treatments being the lowest. Meanwhile, the changing patterns of the C/P ratio of soil microbial biomass at stages of wheat growth are also described.

A model for automatic identification of human pulse signals

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

Artificial Neural Networks Application to Predict Wheat Yield Using Climatic Data

The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.

WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION

Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines.

Improved seismoacoustic analysis model and its application to source parameter inversion of near-surface small-yield chemical explosions

The seismoacoustic analysis method has broad potential applications to source parameter estimation for near-surface explosion events such as industrial explosions and terrorist attacks.In this study,current models were improved by modifying the acoustic model and adopting the Bayesian Markov-chain-Monte-Carlo inversion method.The source parameters of near-surface small-yield chemical explosions were analyzed via the improved seismoacoustic analysis model and by the estimation accuracy of seismoacoustic joint inversion.Estimation and analysis results showed that the improved seismoacoustic analysis model considered ground shock coupling and the impact of explosion products ejecting from the surface so that the improved acoustic impulse relation was more consistent with the measured data than the Ford impulse relation.It is suitable for deep-burial,shallow-burial,and near-surface aerial explosions.Furthermore,trade-off relationships were declined through the application of the improved model to source parameter inversion for near-surface small-yield chemical explosions,and source parameter estimation accuracy was improved.

Fuzzy integral based measurement and assessment of medium and small enterprises'product innovation

A fuzzy integral based way of measurement assessment has been established by using linguistic varia-bles and combining fuzzy integral with hierarchy analysis for measurement of medium and small enterprises'product innovation. The conclusions drawn from analyses made with 20 medium and small enterprises providebases for governments to formulate applicable policies and for medium and small enterprises to enhance theirproduct innovation.

New predictive control algorithms based on Least Squares Support Vector Machines

Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

Balance of multi-wavelets

The discrete scalar data need prefiltering when transformed by discrete multi-wavelet, but prefiltering will make some properties of multi-wavelets lost. Balanced multi-wavelets can avoid prefiltering. The sufficient and necessary condition of p-order balance for multi-wavelets in time domain, the interrelation between balance order and approximation order and the sampling property of balanced multi-wavelets are investigated. The algorithms of 1-order and 2-order balancing for multi-wavelets are obtained. The two algorithms both preserve the orthogonal relation between multi-scaling function and multi-wavelets. More importantly, balancing operation doesnt increase the length of filters, which suggests that a relatively short balanced multi-wavelet can be constructed from an existing unbalanced multi-wavelet as short as possible.

NOVEL LSCM BEAM-FORMING ALGORITHM IN SMART ANTENNA MC-CDMA SYSTEM

A novel Least Squares Constant Modulus (LSCM) beam-forming algorithm in smart antenna Multi-Carrier Code Division Multiple Access (MC-CDMA) system is proposed in this paper. It adaptively beam-forms the multi-carrier antenna array signal using the LSCM Algorithm (LSCMA), and in the meantime, the beam-formed signals on every sub-carrier are combined by using Orthogonal Restore Combination (ORC), Equal Gain Combination (EGC) or Maximum Ratio Combination (MRC). Then the decision of the combined signals and the spread-code of the expected user are used to re-construct the signals on every sub-carrier. At last, the difference between the re-constructed signal and the output signal of the beam-former is used to con-trol the coefficients of the beam-former. The bit error probability of the proposed algorithm is analyzed. We simulated and compared it with the conventional LSCM beam-forming algorithm. Simulation results show that the proposed algorithm is superior to the latter in Bit Error Rate (BER).

NEW LSCM BLIND MULTIUSER DETECTION ALGORITHM FOR ANTENNA ARRAY CDMA SYSTEM

Two blind multiuser detection algorithms for antenna array in Code Division Multiple Access （CDMA） system which apply the linearly constrained condition to the Least Squares Constant Modulus Algorithln （LSCMA） are proposed in this paper. One is the Linearly Constrained LSCMA （LC-LSCMA）, the other is the Preprocessing LC-LSCMA （PLC-LSCMA）. The two algorithms are compared with the conventional LSCMA. The results show that the two algorithms proposed in this paper are superior to the conventional LSCMA and the best one is PLC-LSCMA.

Investigation of different coal types effect on the overall plant recovery

Coal washing plants are usually fed from various sources. Coals include different combinations which should be considered for increasing the plant proficiency. Thus different methods have been used to enrich various coal types. In this study, Alborz-Sharghi coal washing plant was investigated which is fed from five coalmines. The optimum recovery was achieved for all coal types individually through experimental design. The controllable operation parameters in the experiments were collector dosage,frother dosage, solid percent content and particle size. The other parameters such as impeller speed,pH, conditioning time and flotation time were kept constant for all experiments. The optimum combination of coals was also specified. The results show that the optimum recovery for coal blends is 91.2%which shows much improvement relative to the plant conditions.

A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Characterizations of Orthogonal Vector-valued Multivariate Wavelet Packets

In this paper, the notion of orthogonal vector-valued wavelet packets of space L2 （R^s, C^n） is introduced. A procedure for constructing the orthogonal vector-valued wavelet packets is presented. Their properties are characterized by virtue of time-frequency analysis method, matrix theory and finite group theory, and three orthogonality formulas are obtained. Finally, new orthonormal bases of space L2（R^s,C^n） are extracted from these wavelet packets.