THE ADAPTIVE PARAMETER INCREMENTAL METHOD FOR THE ANALYSIS OF SNAPPING PROBLEMS

By the improvement of Riks’and Crisfield’s arc-length method,the adaptiveparameter incremental method is preasted for predicting the snapping response ofstructures. Its justification is fulfilled. Finally,the effectiveness of this method isdemonstrated by solving the snapping response of spherical caps subjected to centrallydistributed pressures.

Continuous on-line adaptation of the rotor time constant in FOC induction machine system

Rotor time constant is an important parameter for the indirect lleld oraentateO control of mauc- tion motor. Incorrect rotor tittle constant value will cause the flux observer generating a wrong angu- lar orientation of the rotor field. A new approach serves for rotor time constant on-line adaptation by setting the stator current to be zero for a short period. A smooth eorrector is designed to prevent ab- normal detection result from making adaptation. Impact of zero current duration on detection error and rotor speed is analyzed by experiments.

Incremental adaptation strategies for agricultural water management under water scarcity condition in Northeast Iran

Population growth and climate changes as two synergistic phenomena have damaged water resources nationwide and worldwide.The water resources available for agriculture is one of the most critical limiting factors in food production enhancement in Iran.Agriculture must tackle the challenge of supplying the increasing demands for food due to population growth.For this purpose,we must get acquainted with how farmers make decisions and their adaptations to water scarcity conditions by studying their adaptation strategies.In this case,we can predict the farmers’behaviors by recognizing the optimal adaptation strategies under water scarcity crisis.This study aims to provide a model to examine and analyze the farmers’behaviors in the realm of water scarcity and the contributing factors to it,facilitating a better understanding of the potential implications of such scarcity,introducing the adaptation changeability procedures for agricultural water management and assistance to farmers to adapt,and developing the actions for mitigating the vulnerability of agriculture sector in Iran.In this study,175 farmers in Northeast Iran were interviewed to conduct an applied research by questionnaire survey.The research tool was a researcher-made questionnaire designed based on the theoretical literature and designed model.According to the research,the model of incremental adaptation strategies could determine the effects of a set of psychological and socioeconomic factors and the formation of adaptation decisions in water resources management under water scarcity conditions.The results indicated that the concern and attitude,farmers’knowledge and technical skill,self-efficiency and risk-taking level,social capital and information availability for farmers,and the internal norm are significantly and positively correlated in this model.The results provide a proper understanding of farmers’sustainable decision-making and a perspective on contributing factors to the strategy framework and improved policies in the form of incremental adaptation strategies when facing water scarcity in agricultural water resources management.Thus,this study will respond to deal with water scarcity by changing farmers’behaviors in dry lands and water scarce regions.

Incremental Diversity: A Framework for Rate-Adaptation/Energy-Conservation Enhancement in MIMO Systems

In recent years, MIMO technology has emerged as one of the technical breakthroughs in the field of wireless communications. Two famous MIMO techniques have become investigated thoroughly throughout the literature;Spatial Multiplexing, and Space Time Block Coding. On one hand, Spatial Multiplexing offers high data rates. On the other hand, Space Time Block Coding presents transmission fidelity. This imposes a fundamental tradeoff between capacity and reliability. Adaptive MIMO Switching schemes have been proposed to select the MIMO scheme that best fits the channel conditions. However, the switching schemes presented in the literature directly switch between the MIMO endpoints. In this paper, an adaptive MIMO system that incrementally switches from multiplexing towards diversity is proposed. The proposed scheme is referred to as incremental diversity and can be set to operate in two different modes;Rate-Adaptive, and Energy-Conservative Incremental Diversity. Results indicate that the proposed incremental diversity framework achieves transmission reliability offered by MIMO diversity, while maintaining a gradual increase in spectral efficiency (in the Rate-Adaptive mode) or a reduction in required number of received symbols (in the Energy-Conservative mode) with increase in the SNR.

Ad Hoc中的TCP改进方案——Adaptive ADTCP

在分析无线自组网特点及其对TCP性能影响的基础上,提出了一种能够自适应无线自组网状态的TCP改进方案(Adaptive ADTCP)。Adaptive ADTCP在明确辨识网络状态的前提下,首先根据前向路径跳数自适应地调整拥塞窗口增长因子,限制源节点TCP拥塞窗口过分增长,避免造成网络拥塞;同时源节点TCP根据当前拥塞窗口自适应改变发送的分组长度,充分利用网络资源。仿真实验表明,在网络重负载和节点高速移动情况下,Adaptive ADTCP对数据传输的吞吐量有较大提升。

IMPROVED FXLMS ALGORITHM BASED ON ADAPTIVE NOTCH FILTER APPROACH

An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, the convergent speed of the on-line control path identification process is significantly improved. As a result, the controller performance is greatly enhanced. Besides the algorithm development, some important factors, such as the influence of reference signal on the controller convergent speed, are also discussed. The effectiveness of the algorithm is verified by experimental results.

Equating incremental dynamic analysis with static nonlinear analysis at near-field excitation

In an incremental dynamic analysis(IDA) using a set of ground motion records,nonlinear time history analysis needs to be performed on structures.It is well recognized that IDA calls for high computational efforts and the results are highly sensitive to selected ground motions.As a result,alternative static methods are needed.This study aims to introduce a new double-stage(N1- N2) static method to estimate capacity curves of MR frames.The technique is regulated to resemble IDA results with specific emphasis on near-field ground motions.Using an ensemble of 56 near-field earthquake records,required ID As have been carried out for SAC-Los Angeles 3-,9- and 20-story buildings and an additional 15-story building.The results of the proposed static method are compared with those from IDA,displacement-based adaptive procedure(DAP),and multimodal procedure(MMP).The results indicate that in addition to enhanced accuracy,very little time is required in the case of N1-N2 method.Thus,for the 3-story structure,the time required is less than 1 minute.The proposed N1-N2 method shows the best accuracy in terms of lateral mechanisms for the 15-story frame while for the other cases,the first mode load pattern leads to the best accuracy.

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network(NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.

ADAPTIVE AND ACTIVE COMPUTING PARADIGM FOR PERSONALIZED INFORMATION SERVICE IN DISTRIBUTED HETERONGEOUS ENVIRONMENT

To solve the problem that traditional pull based information service can’t meet the demand of long term users getting domain information timely and properly, an adaptive and active computing paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user centered, push based higsh quality information service timely in a proper way, the motivation of which is generalized as R 4 Service: the right information at the right time in the right way to the right person, upon which formalized algorithms framework of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push based, event driven, interest related, adaptive and active information service mode, which is useful and promising for long term user to gain fresh information instead of polling from kinds of information sources.

Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking(MPPT)techniques for Photo-Voltaic based Battery Storage Systems(PV-BSS).To have a full comparative study in terms of the dynamic response,battery state of charge(SOC),and oscillations around the Maximum Power Point(MPP)of the PV-BSS to variations in climate conditions,these techniques are simulated in Matlab/Simulink.The introduced methodologies are classified into two types;the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb&Observe and Incremental Conductance techniques.The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre-build Adaptive Neuro-Fuzzy Inference System(ANFIS)model to predict DC–DC converter optimum duty cycle to track MPP.Then evaluation study is introduced for conventional and proposed On-Line MPPT techniques.This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PV-BSS.Also the introduced model can be used as a valued reference model for future research related to Soft Computing(SC)MPPT techniques.A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

三阶段自适应采样和增量克里金辅助的昂贵高维优化算法

代理辅助进化算法已广泛应用于求解代价高昂的多目标优化问题,但大多数由于代理模型的局限性而仅限于解决决策变量低维的问题。为了解决高维的昂贵多目标优化问题,提出了一种基于三阶段自适应采样策略的改进增量克里金辅助的进化算法。该算法使用改进的增量克里金模型来近似每个目标函数,此模型的超参数根据预测的不确定性进行自适应更新,降低计算复杂度的同时保证模型在高维上的准确性;此外,在模型管理方面提出一种三阶段自适应采样的策略,将采样过程分为不同的优化阶段以更有针对性的选择个体,能够首先保证收敛性,提高算法的收敛速度。为了验证算法的有效性,在包含各种特征的两组测试问题DTLZ(deb-thiele-laumanns-zitzler)、MaF(many-objective function)和路径规划实际工程问题上与最新的同类型算法进行实验对比,结果表明该算法在解决决策变量高维的昂贵多目标优化问题上具有较强的竞争力。

热风洞温度系统的无模型自适应级联控制

热风洞能够为高温热电偶动态标定和航空发动机叶片热强度测试等应用场景提供均匀且稳定的温度场,如何对热风洞所产生的热气流进行宽温范围的温度控制是一个难点。为此,建立了热风洞燃油流量和燃烧室温度的数学模型;设计了一种内环为增量式比例积分微分(PID)和外环为无模型自适应控制(MFAC)的级联控制方法,增量式PID控制器控制燃油流量,MFAC控制器控制燃烧室温度,实现了对燃烧室温度的有效控制;进行了不同目标温度的数值模拟和试验研究,并与FuzzyPID-PID级联控制方案和传统的级联PID控制方案进行了对比测试。试验结果确认了所提控制方案的可行性与优越性。

火箭发动机故障检测的快速增量单分类支持向量机算法

为解决液体火箭发动机故障诊断正负样本不平均问题,以及实现发动机稳态工作段自适应故障检测,建立了基于快速增量单分类支持向量机的异常检测模型。采取特征工程方法,对传感器获得的多变量时间序列进行特征提取。通过增量学习方法,对单分类支持向量机模型进行改进,并应用于液体火箭发动机异常检测,使单分类支持向量机检测模型具备对不同台次、不同工况的自适应性,提高了模型的计算速度。对多台次热试车数据的分析结果表明,该模型十分有效,训练速度快,具备实用价值。

光电倍增管计数温漂自适应补偿方法

光电倍增管是微光探测的重要器件,使用光子计数法对光强进行检测可以大幅降低噪声的干扰,但光电倍增管的阴极灵敏度、增益及暗噪声易受环境温度的影响,导致输出脉冲幅值的不稳定,影响系统对微光的检测性能。本文通过使用微控制单元、数字模拟转换器等模块对光电倍增管设计了高压增益补偿和阈值校正系统,通过对不同温度下光电倍增管的阴极饱和电流、灵敏度和暗噪声的综合分析,构建温度与高压补偿增量Vh及阈值补偿增量Vt混合函数模型,使用补偿增量混合模型对光电倍增管计数输出进行自适应温漂补偿。对滨松的端窗型光电倍增管CR135进行实验,证明了此系统在较大温度变化下仍具有良好的输出信噪比,在-20℃时计数率平均提升0.19,即使引入了少量暗噪声,仍大幅提升了探测性能。

基于M-K模型计算FLC无条件收敛新算法

M-K失稳模型的无条件收敛与快速求解对成形极限曲线(forming limit curve, FLC)的获取具有重要的理论意义与研究价值,也是理论FLC计算的难点之一.然而,在M-K失稳模型框架内基于复杂屈服准则计算成形极限曲线时,采用传统牛顿迭代算法无法保证收敛,而固定增量法则计算耗时过长,严重制约了M-K失稳模型的应用场景.因此,文章以锆合金为实验材料,提出了一种自适应增量算法,该算法通过迭代误差信息与震荡情况自适应调整迭代步长,不仅能够保证计算精度,满足无条件收敛,还能大幅提升计算速度.此外,本研究还验证了采用自适应增量法计算锆合金FLC的准确性,并分析了算法的迭代误差Err与初始应变增量对FLC预测结果的影响.结果表明:自适应增量法具有无条件保证收敛、计算速度快以及计算结果不受初始迭代步长影响的优势,其计算耗时仅为固定增量法的0.44%;此外,本研究认为初始参数中迭代误差小于10-6、初始应变增量小于0.001是得到稳定的FLC结果的前提条件;根据对锆合金FLC预测结果可知,在M-K模型框架内基于Yld2000-2d屈服准则理论采用自适应增量法计算可以很好地描述锆合金的塑性行为.由于自适应增量迭代法简单易用且计算高效,有望应用于有限元软件中子程序的开发,加速其计算速度并提高锆合金数值模拟精度.

基于增量模型的PMSM鲁棒性模型预测控制算法研究

永磁同步电机模型预测电流控制(MPCC)策略因具有良好的动态性能而得到了广泛应用,但该策略较为依赖模型参数的准确性。为了在保持控制系统的动态性能的同时提高系统的参数鲁棒性,提出了一种基于三矢量的增量型MPCC策略。在MPCC中引入基于d轴电流的模型参考自适应电感辨识算法,并对自适应律进行了分析。基于Matlab/Simulink进行了仿真,结果表明所提策略能够实现在保持三矢量预测控制动态性能的前提下,减小磁链参数失配造成的预测误差;基于d轴电流的模型参考自适应算法可以进行快速稳定地电感参数辨识,有效地提高了控制系统的参数鲁棒性。

Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.

考虑风光出力不确定性的风光储微电网系统智能协调控制策略

对风光储互补的微电网发电系统进行研究,针对风光发电与负载功率不匹配问题,提出了一种基于新型变步长电导增量法的最大功率点追踪控制方法,利用基于Ziegler-Nichols法的改进自适应PID算法实现负载功率蓄电池充放电控制的协同控制策略。通过在MATLAB/Simulink中搭建仿真模型并进行验证,证明了所述方案的准确性以及可行性。

基于优化八叉树的场景视锥体裁剪算法

大体量3D模型容易导致浏览器端渲染帧数低、显示卡顿及资源消耗大等问题,其原因是这类模型通常包含数以亿计的三角面片,在有限的时间内无法实现快速加载与渲染。针对此类问题,提出一种基于优化八叉树的场景视锥体裁剪算法。该算法采用地址码(Morton码)、节点视距标准和按需增量划分技术,使得八叉树具有自适应性与良好的压缩效率;采用双层包围体和基础相交测试技术,提高视锥体裁剪的精确性,整体上实现提升渲染帧数、显示流畅的目标。高速列车实例模型研究表明,与传统八叉树视锥体裁剪算法相比,所提算法平均渲染帧数上提高了约14帧,空间压缩率提高了37.8个百分点。

进化网络模型:无先验知识的自适应自监督持续学习

无监督持续学习(UCL)是指能够随着时间的推移而学习,同时在没有监督的情况下记住以前的模式。虽然在这个方向上取得了很大进展,但现有工作通常假设对于即将到来的数据有强大的先验知识(例如,知道类别边界),而在复杂和不可预测的开放环境中可能无法获得这些知识。受到现实场景的启发,该文提出一个更实际的问题设置,称为无先验知识的在线自监督持续学习。所提设置具有挑战性,因为数据是非独立同分布的,且缺乏外部监督、没有先验知识。为了解决这些挑战,该文提出一种进化网络模型(英文名EvolveNet),它是一种无先验知识的自适应自监督持续学习方法,能够纯粹地从数据连续体中提取和记忆表示。EvolveNet围绕3个主要组件设计:对抗伪监督学习损失、自监督遗忘损失和在线记忆更新,以进行均匀子集选择。这3个组件的设计旨在协同工作,以最大化学习性能。该文在5个公开数据集上对EvolveNet进行了全面实验。结果显示,在所有设置中,EvolveNet优于现有算法,在CIFAR-10,CIFAR-100和TinyImageNet数据集上的准确率显著提高,同时在针对增量学习的多模态数据集Core-50和iLab-20M上也表现最佳。该文还进行了跨数据集的泛化实验,结果显示Evolve-Net在泛化方面更加稳健。最后,在Github上开源了EvolveNet模型和核心代码,促进了无监督持续学习的进展,并为研究社区提供了有用的工具和平台。