如何帮助中专新生度过适应期

中等学校的新生在心理和行为上往往会产生某些偏差和诸多不适应现象。其主要表现在以下几方面：１．对中专学业、管理制度等方面的认识偏差。具体有两种表现：一是感到脱离了繁忙而紧张的中考阶段，有松口气的想法，认为在学业上不必“分分计较”，学习的主动性不强，有时...

基于BP神经网络的库存动态预测及其应用

利用库存的历史数据,建立了动态环境下一个基于BP神经网络的初始库存模型,采用提前结束训练法克服因样本量不足而产生的网络过适应问题,并通过网络的训练获得一个关系简单的库存模型。每次获得新样本数据后对模型进行重构,达到了动态建模和分析的目的,并用实例进行了说明,为库存系统的管理决策提供了理论依据。

改进滑动窗口核主元分析算法

传统滑动窗口主元分析算法在处理工程参数缓慢劣化问题时存在过适应现象,且容易产生误报、漏报的问题。为此,本文以滑动窗口核主元分析(KPCA)算法为基础提出一种基于数据块的改进滑动窗口核主元分析算法。该算法采用MATLAB软件进行数据标准化预处理,利用迭代法剔除采样数据异常值;在检测过程中以数据块为单位进行模型更新,通过调节数据块的大小,使故障诊断更加快速、准确。将该算法和传统核主元算法分别应用于风力发电机组实验数据分析,验证了本文所提出的改进算法能够更好地解决参数缓慢劣化问题。

决策树后剪枝算法的研究

决策树学习策略广泛应用于模式识别和机器学习等领域 ,用来解决与分类相关的问题。决策树剪枝的作用是简化决策树 ,提高决策树的泛化能力 ,避免对训练集的过适应 ,是决策树学习中的重要研究内容。本文详细描述了常用的四种后剪枝算法 ,分析了后剪枝技术的研究与发展现状 ,为具体应用中选择剪枝算法提供了一定的理论基础。

Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

Application of Fuzzy Modification Strategy in MRAC

Based on a simplified model reference adaptive control(SMRAC) algorithm a parameter modification algorithm according to fuzzy laws is proposed in this paper. The method makes the adaptive parameters in SMRAC only rely on the status of performance error. Thus it eliminates the influences of gain coefficients in SMRAC and the amplitude of input signal on the dynamic characteristics. Experiments on various step amplitudes and loads show that the performances of SMRAC are improved by incorporating fuzzy modification method.

消息通道

Bay Networks已加入一个独特的软硬件互操作性测试论坛——Microsystems Connectathon 98,以加强其通过适应性网络提供透明的、可伸缩的技术和互操作性的承诺。 Connectathon是一个提供基础设施的网络,允许厂商对操作性解决方案进行测试,Sun已选择BayNetworks为Connectathon 98提供基础设施,通过Bay

Study on the Adaptability of Introduced Lippia nodiflora in the Rock Desertifation Area in Chongqing

With intensifying of karst rock desertification in Southwest China, the techniques and modes about taming of karst rock desertification are increasingly rich. But some methods and technologies were hard to transplant. According to the concrete conditions of karst rock desertification in Chongqing, Lippia nodiflora(L.) Greene was introduced as a kind of pioneer plant. By way of the cultivation of introduced practice in Nanchuan District and Wushan County, the phenophase, growth rate and resistibility of Lippia nodiflora were tested. The results show that Lippia nodiflora was suitable for being promoted in karst rock desertification areas, for it’s rapid growth, drought-relief, low death rate and adaptability to calcium in soil.

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Virtual reconfigurable architecture for evolving combinational logic circuits

A virtual reconfigurable architecture(VRA)-based evolvable hardware is proposed for automatic synthesis of combinational logic circuits at gate-level.The proposed VRA is implemented by a Celoxica RC1000 peripheral component interconnect(PCI)board with an Xilinx Virtex xcv2000E field programmable gate array(FPGA).To improve the quality of the evolved circuits,the VRA works through a two-stage evolution: finding a functional circuit and minimizing the number of logic gates used in a feasible circuit.To optimize the algorithm performance in the two-stage evolutionary process and set free the user from the time-consuming process of mutation parameter tuning,a self-adaptive mutation rate control(SAMRC)scheme is introduced.In the evolutionary process,the mutation rate control parameters are encoded as additional genes in the chromosome and also undergo evolutionary operations.The efficiency of the proposed methodology is tested with the evolutions of a 4-bit even parity function,a 2-bit multiplier,and a 3-bit multiplier.The obtained results demonstrate that our scheme improves the evolutionary design of combinational logic circuits in terms of quality of the evolved circuit as well as the computational effort,when compared to the existing evolvable hardware approaches.

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.

The training process： Planning for strength-power training in track and field. Part 2： Practical and applied aspects

Planning training programs for strength-power track and field athletes require an understanding of both training principles and training theory. The training principles are overload, variation, and specificity. Each of these principles must be incorporated into an appropriate system of training. Conceptually, periodization embraces training principles and offers advantages in planning, allowing for logical integration and manipulation of training variables such as exercise selection, intensification, and volume factors. The adaptation and progress of the athlete is to a large extent directly related to the ability of the coach/athlete to create and carry an efficient and efficacious training process. This ability includes： an understanding of how exercises affect physiological and performance adaptation （i.e., maximum force, rate of force development, power, etc.）, how to optimize transfer of training effect ensuring that training exercises have maximum potential for carryover to performance, and how to implement programs with variations at appropriate levels （macro, meso, and micro） such that fatigue management is enhanced and performance progress is optimized.

Research and Design of the Adaptive Evaluation System Based on the IRT Theory

This paper, on the basis of the author realizing the skill evaluation system based on real environment, discusses several commonly used parameter estimation methods based on item response theory （ IRT ） and analyzes the advantages and disadvantages of each estimation method and their respective application fields. Also, it expounds the research theory and design process of skill adaptive evaluation system based on real environment and the innovation of the system.

Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.

Transition Stage Control of Tail Sitter Aircraft Based on Guardian Maps

To deal with the high nonlinearities and strong couplings in the transition stage of tailsitter aircraft,an adaptive gainscheduling controller is proposed by combining the guardian maps theory and H∞control theory.This method is applied to track the flightpath angle of the transition stage of tailsitter aircraft,and compared with the linear quadratic regulator(LQR)method based on traditional gain scheduling.Simulation results show that the controller based on the guardian maps theory can autonomously schedule the appropriate control parameters and accomplish the stable transition.Besides,the proposed method shows better tracking performance than the LQR method based on traditional gain scheduling.

Translation and Enculturation as a Process of Acculturation

When we look through the world history, it can be seen clearly that language has a great role on culture, arts, and social movements, and the translation is an important player in this context. A commonly shared European culture together with its values has emerged as a product of such sociolinguistic dynamics. Following these encounters, whether at word borrowing level or morpho-syntactical level, European languages have had positive and/or negative effects on each other and have evolved ever since in this way as they have permeated themselves into culture. From the point of view on translation＇s intermediary role in enabling interaction between cultures throughout the history, the aim of the present study is to problematize the answers to the following questions： What are cultural ramifications that stem from linguistic encounter？ What are the contributions of translated language to acculturation and enculturation processes？ Can the new information through translation produce a culture translation phenomenon？ How the hybrid understanding functions？ Translation itself is a language encounter that makes impact on targeted languages as well as on its source. In this study, the dynamics that form this encounter space as a meta textual phenomenon has been problematized.

Adaptive Gaussian Noise Image Removal Algorithm Using Filtering-Based Noise Estimation

This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise density.To adaptively deal with variety of the noise amount,a noisy input image is firstly filtered by a lowpass filter.Standard deviation of the noise is computed from different images between the noisy input and its filtered image.In addition,a modified Gaussian noise removal filter based on the local statistics such as local weighted mean,local weighted activity and local maximum is used to control the degree of noise suppression.Experiments show the effectiveness of the proposed algorithm.