Amplitude phase control for electro-hydraulic servo system based on normalized least-mean-square adaptive filtering algorithm

The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.

基于鱼群算法优化normalized cut的彩色图像分割方法

为了克服传统的谱聚类算法求解normalized cut彩色图像分割时,分割效果差、算法复杂度高的缺点,提出了一种基于鱼群算法优化normalized cut的彩色图像分割方法。先对图像进行模糊C-均值聚类预处理,然后用鱼群优化算法替代谱聚类算法求解Ncut的最小值,最后通过最优个体鱼得到分割结果。实验表明,该方法耗时少,且分割效果好。

一种基于遗传算法的Normalized Cut准则图像分割方法

Normalized Cut准则图像分割是基于图论的方法,是一种比较典型的规范化分割,但由于其运算量较大、收敛条件难以控制等缺陷,使得图像二值化分割不均匀。本文从Normalized Cut缺陷研究入手,利用遗传算法全局快速检索特性,改进Normalized Cut优化函数,获得精度理想的图像分割效果。

An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation

In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.

An Algorithm for the Feedback Vertex Set Problem on a Normal Helly Circular-Arc Graph

The feedback vertex set (FVS) problem is to find the set of vertices of minimum cardinality whose removal renders the graph acyclic. The FVS problem has applications in several areas such as combinatorial circuit design, synchronous systems, computer systems, and very-large-scale integration (VLSI) circuits. The FVS problem is known to be NP-hard for simple graphs, but polynomi-al-time algorithms have been found for special classes of graphs. The intersection graph of a collection of arcs on a circle is called a circular-arc graph. A normal Helly circular-arc graph is a proper subclass of the set of circular-arc graphs. In this paper, we present an algorithm that takes time to solve the FVS problem in a normal Helly circular-arc graph with n vertices and m edges.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

应用Normal矩阵谱平分法的多社团发现

现实世界中许多实际网络都有一个共同的性质,即社团结构。揭示网络中的社团结构,对于了解网络结构与分析网络性质都是很重要的。分析了常见的社团发现算法的特点,以及谱二分法在实际应用中必须不断迭代才能完成多社团发现的不足,提出了基于Normal矩阵和k-means聚类算法的多社团发现方法。该算法能选择合适的特征向量维数,为k-means划分社团提供有效数据,相比其他算法有着较高的准确率。

Poisson Log-normal回归模型的影响评价(英文)

本文利用EM算法研究了来自于Lognormal分布权重的混合Poisson模型,即Poisson Lognormal回归模型,从而利用基于完全数据似然函数的条件期望进行影响评价.基于数据删除模型和局部影响分析方法,分别得到了广义Cook距离、Q距离和三种不同扰动模型下的正则曲率度量等诊断统计量.

三参数Normal-Ogive模型参数估计的SAEM算法

Normal-Ogive模型是项目反应理论的代表性模型之一,其参数估计主要是基于MCMC抽样实现的,但MCMC抽样的不足是计算效率较低。针对这一问题,本文以混合模型(mixture model)的视角,通过变量扩充,提出三参数normalogive(3PNO)模型题目参数估计的随机逼近EM(stochastic approximation EM,简称SAEM)算法,并通过Monte Carlo模拟对SAEM算法的主要影响因素、计算效率、估计的返真性进行验证。模拟研究的结果表明:SAEM算法能够准确实现3PNO模型题目参数估计的计算,并且具有较高的计算效率,表现出优良的计算性质。

A novel algorithm of adaptive IIR lattice notch filter and performance analysis

A novel adaptive algorithm of IIR lattice notch filter realized by all-pass filter is presented. The time-averaged estimation of cross correlation of the present instantaneous input signal and the past output signal is used to update the step-size, leading to a considerably improved convergence rate in a low SNR situation and reduced steady-state bias and MSE. The theoretical expression for steady-state bounds on the step-size is derived, and the influence factors on the stable performance of the algorithm theoretically are analyzed. A normalized power factor is then introduced to control variation of step-size in its steady-state bounds. This technique prevents divergence due to the influence of large power input signal and improves robustness. Numerical experiments are performed to demonstrate superiority of the proposed method.

MI-NLMS adaptive beamforming algorithm for smart antenna system applications

A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.

Finite Mixture Normal Models, with Application to Dose-Response Studies

In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response studies, a class of phenomena that frequently occur are that experimental subjects (e.g., mice) may have different responses like ’none, mild, severe’ after a toxicant experiment, or ’getting worse, no change, getting better’ after a medical treatment, etc. These phenomena have attracted the attention of many researchers in recent years. Finite

Optimization method of fi rst-arrival waveform inversion based on the L-BFGS algorithm

The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In the full waveform inversion method,adding a cross-correlation function to the objective function can eff ectively reduce the nonlinearity of the inversion process.In this paper,the nonlinearity of this process is reduced by introducing the correlation objective function into the FAWI and by deriving the corresponding gradient formula.We then combine the first-arrival wave travel-time tomography with the FAWI to form a set of inversion processes.This paper uses the limited memory Broyden-Fletcher-Goldfarb-Shanno(L-BFGS)algorithm to improve the computational effi ciency of inversion and solve the problem of the low effi ciency of the FAWI method.The overthrust model and fi eld data test show that the method used in this paper can eff ectively reduce the nonlinearity of inversion and improve the inversion calculation effi ciency at the same time.

Simulations of the Performance of Maximum Power Point Tracking Algorithms Based on Experimental Data According to the Topologies of DC-DC Converters

Maximum Power Point Tracking (MPPT) algorithms are now widely used in PV systems independently of the weather conditions. In function of the application, a DC-DC converter topology is chosen without any previous performance test under normal weather conditions. This paper proposes an experimental evaluation of MPPT algorithms according to DC-DC converters topologies, under normal operation conditions. Four widely used MPPT algorithms i.e. Perturb and Observe (P & O), Hill Climbing (HC), Fixed step Increment of Conductance (INCF) and Variable step Increment of Conductance (INCV) are implemented using two topologies of DC-DC converters i.e. buck and boost converters. As input variables to the PV systems, recorded irradiance and temperature, and extracted photovoltaic parameters (ideality factor, series resistance and reverse saturation current) were used. The obtained results show that buck converter has a lot of power losses when controlled by each of the four MPPT algorithms. Meanwhile, boost converter presents a stable output power during the whole day. Once more, the results show that INCV algorithm has the best performance.

Impact of dose calculation algorithm on radiation therapy

The quality of radiation therapy depends on the ability to maximize the tumor control probability while minimizing the normal tissue complication probability.Both of these two quantities are directly related to the accuracy of dose distributions calculated by treatment planning systems.The commonly used dose calculation algorithms in the treatment planning systems are reviewed in this work.The accuracy comparisons among these algorithms are illustrated by summarizing the highly cited research papers on this topic.Further,the correlation between the algorithms and tumor control probability/normal tissue complication probability values are manifested by several recent studies from different groups.All the cases demonstrate that dose calculation algorithms play a vital role in radiation therapy.

A parametric bootstrap approach for one-way classification model with skew-normal random effects

In this paper,several properties of one-way classification model with skew-normal random effects are obtained,such as moment generating function,density function and noncentral skew chi-square distribution,etc.Based on the EM algorithm,we discuss the maximum likelihood(ML)estimation of unknown parameters.For testing problem of fixed effect,a parametric bootstrap(PB)approach is developed.Finally,some simulation results on the Type I error rates and powers of the PB approach are obtained,which show that the PB approach provides satisfactory performances on the Type I error rates and powers,even for small samples.For illustration,our main results are applied to a real data problem.

Genetic Algorithms of Structural Fuzzy Reliability Index

In this paper the simple generation algorithms are improved. According to the geometric meaning of the structural reliability index, a method is proposed to deal with the variables in the standard normal space. With consideration of variable distribution, the correlation coefficient of the variables and its fuzzy reliability index, the feasibility and the reliability of the algorithms are proved with an example of structural reliability analysis and optimization.

An improved form of the ELMS algorithm

ELMS algorithm is the first two-channel adaptive filtering algorithm that takes into account the cross-correlation between the two input signals. The algorithm does not preprocess input signals, so it does not degrade the quality of the speech. However, a lot of computer simulation results show that ELMS algorithm has a bad performance. The ELMS algorithm is analyzed firstly, then a new algorithm is presented by modifying the block matrix used in ELMS algorithm to approximate input signals self-correlation matrix. The computer simulation results indicate that the improved algorithm has a better behavior than the ELMS algorithm.

A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed.An important issue that arises is in that these devices suffer from security attacks.To deal with it,we turn to the blockchain technologies.The note in the alliance chain need rules to limit write permissions.Alliance chain can provide security management functions,using these functions to meet the management between the members,certification,authorization,monitoring and auditing.This article mainly analyzes some requirements realization which applies to the alliance chain,and introduces a new consensus algorithm,generalized Legendre sequence(GLS)consensus algorithm,for alliance chain.GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues.In the alliance chain,GLS consensus algorithm can complete node address hiding,automatic task sorting,task automatic grouping,task node scope confirmation,task address binding and stamp timestamp.Moreover,the GLS consensus algorithm increases the difficulty of network malicious attack.