Buck-Boost矩阵变换器主电路参数随电流定额的自适应优选方法

针对Buck-Boost矩阵变换器（BBMC）在不同额定输出电流下的主电路参数优化设计问题,提出了一种BBMC主电路参数随其额定电流变化的自适应优选方法.通过建立BBMC优化目标与优化对象间的数学模型,研究基于自适应狼群优化算法的BBMC主电路参数优化设计方法;在此基础上进一步研究确定BBMC主电路优化设计参数与BBMC额定输出电流间的变化规律,为实现不同电流定额下BBMC主电路的优化设计奠定基础;最后通过仿真对上述理论分析进行了验证.     

复杂需求环境下中小型制造企业作业系统建模与分析

针对中小型制造企业（small medium production enterprises,SMPEs）对复杂需求的响应速度普遍比较慢的问题,从调整系统参数合理性的角度提出了一种提高系统响应速度的方法.首先,构建了融合员工关键技能的SMPEs生产作业系统;其次,基于连续时间马尔可夫链建立系统的稳态平衡方程模型,在采用两种不同控制策略的情况下,深入研究了生产线的切换时间等参数对系统性能的影响;最后,在给定的生产实例下,通过理论推导和仿真实验分析了系统性能指标随着关键参数的变化趋势.实验结果表明,理论推导结果与实际生产的运行情况吻合良好,为SMPEs生产企业进行合理的系统参数设计提供了重要的基础.     

Affine parameter estimation from the trace transform

In this paper, we assume that we are given the images of two segmented objects, one of which may be an affinely distorted version of the other, and wish to recover the values of the parameters of the affine transformation between the two images. The images may also differ by the overall level of illumination. The multiplicative constant of such difference may also be recovered. We present a generic theoretical framework to solve this problem. In terms of this framework, other proposed methods may be interpreted. We show how, in this framework, one can recover the affine parameters in a way that is robust to various effects, such as occlusion and illumination variation. The proposed method is generic enough to be applicable also to matching two images that do not depict the same scene or object.     

Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation. Total variation provides well-preserved edge features, but suffers from staircase effects in smooth regions, whereas high-order total variation can alleviate staircase effects. Moreover, we introduce a strategy for adaptively selecting regularization parameters. We develop an efficient alternating minimization algorithm for solving the proposed model. Numerical examples suggest that the proposed method has the advantages of better preserving edges and reducing staircase effects.     

基于MATLAB化工装置的操作弹性分析

阐述了化工装置操作弹性分析的必要性和实用方法。以浮阀塔板为例,说明如何采用顶点法求解化工装置弹性系数,进行弹性分析,确定不确定参数真实可行的变化范围,比较不同设计的弹性大小;并找出瓶颈所在,为装置的扩产改造提供理论依据。采用MATLAB编程求解弹性系数,算法简单,语言精练。本文所介绍的算法对求解化工装置的弹性系数具有通用性。     

Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal

Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this letter, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal input is split into fast (within-interval) and slow (between-intervals) components. A parameter estimation method is proposed, allowing the parameters to be estimated simultaneously over the entire data set. This increases the statistical power, and the average estimate over all ISIs will be improved in the sense of decreased variance of the estimator compared to previous approaches, where the estimation has been conducted on each individual ISI. The results obtained on real data show good agreement with classical regression methods.     

Stability and convergence of the two parameter cubic spline collocation method for delay differential equations

In this paper, we propose the cubic spline collocation method with two parameters for solving delay differential equations (DDEs). Some results of the local truncation error and the convergence of the spline collocation method are given. We also obtain some results of the linear stability and the nonlinear stability of the method for DDEs. In particular, we design an algorithm to obtain the ranges of the two parameters α,β which are necessary for the P-stability of the collocation method. Some illustrative examples successfully verify our theoretical results.     

Theoretical analysis on feature extraction capability of class-augmented PCA

In this paper, we present a theoretical analysis on a novel supervised feature extraction method called class-augmented principal component analysis (CA-PCA), which is composed of processes for encoding the class information, augmenting the encoded information to data, and extracting features from class-augmented data by applying PCA. Through a combination of these processes, CA-PCA can extract features appropriate for classification. Our theoretical analysis aims to clarify the role of these processes and to provide an explanation on how CA-PCA can extract good features. Experimental results for various datasets are provided in order to show the validity of the proposed method for real problems. The effect of parameters on the quality of extracted features is also investigated and the rules of thumb for determining the appropriate parameters are provided.     

Propulsion modeling of caudal fin driving system on balloon fish robot

Recently, biomimetic robots combining the characteristics of fish robots and airship robots have been studied. In this work, we consider balloon fish robots (BFR). This paper presents our understanding of the BFR motion obtained through simulations and experimental results. The simulation of the BFR motion is based on a traveling-wave equation, defined as the product of a sine wave and quadratic curve. In this study, we derived an equation of motion for BFR and conducted an experiment to measure the thrust force. By solving the equation of motion with the Runge–Kutta method, we are able to calculate theoretical values for the propulsion velocity of BFR. We validate the simulation by comparing theoretical and experimental propulsion speed values. As a future task, we will measure BFR thrust at more parameters.     

Optimal Design of a 6-DOF Parallel Manipulator Using Particle Swarm Optimization

Performance indices of parallel manipulators (PMs) vary widely with the variation of geometric properties. Improvement of one parameter often leads to worsen the other parameters. Therefore, getting into an optimum design for the PMs has been subject of much recent research. In this paper, we optimize three performance parameters of a PM simultaneously including workspace, condition number, and stiffness. In addition, a new performance index is introduced for stiffness evaluation of the PMs. The index is invariant under similarities. Because of complexity of cost function and number of variables, choosing an optimization method that can converge to the optimum point is very important. We select particle swarm optimization (PSO) method and show that this algorithm is perfect for performance optimization of PMs. Furthermore, we propose a new subroutine added to PSO algorithm to improve its convergence.     

Computer program for automated sleep depth estimation

In this article, we present a new implementation of an amplitude-independent method for continuous-scale sleep depth estimation. Having been implemented as an add-on analysis module under commercially available biosignal recording and analysis software, it can be easily applied in clinical routine. The software gives the user full freedom to change all the analysis parameters inside theoretical limits. Computational sleep depth profiles produced by the presented software compare favourably with visual classifications. Future work will concentrate on systematic optimization of analysis parameters, further evaluation of the method with disturbed sleep and application of the method for automated adaptive sleep analysis.     

GPRS网络中综合业务双速漏桶算法研究

提出了一种针对GPRS网络中综合业务流量控制方法——双速漏桶算法。并利用流体流法分析了双速漏桶监管的性能，得到实时业务和非实时业务分组丢失率和平均延时的理论公式，数值计算结果表明选用合适的漏桶参数可以进行有效的流量控制。     

Operational estimation of primary production at large geographical scales

A protocol is developed for calculation of phytoplankton production from remotely-sensed data in the operational mode. The key element is an objective assignment, on a pixel-by-pixel basis, of the parameters required to implement a primary production model (parameters of the photosynthesis-response function and of the vertical distribution of pigment biomass). In a regional context, the assignment is made by searching the archived data on these parameters according to the (remotely-sensed) chlorophyll concentration and surface temperature. We refer to this approach as the Nearest-Neighbour Method. The procedure is justified on the basis of the known variation of bio-optical properties of phytoplankton with chlorophyll and temperature as well as through consideration of the seasonal variation of watercolumn stratification and its effect on the vertical pigment profile. We illustrate the method, and its justification, using data from the Northwest Atlantic Ocean. Using data from an oceanographic expedition not included in the archive, we find that the parameters estimated in this way are not significantly different from those obtained by direct measurement. We estimate the error associated with parameter assignment on the calculated phytoplankton production to be about 27%. Some potential limitations of the method are discussed.     

Adaptive inverse compensation for actuator backlash with piecewise time-varying parameters

Existing adaptive inverse compensation methods for cancelling actuator backlash nonlinearity are all restricted to handle constant backlash parameters. In other words, when discontinuity and time variation as both ubiquitous phenomena in practical actuators exist, such inverse compensation methods are no longer applicable theoretically. So far, no result has been reported in addressing such an issue, regardless of its importance in practice. In this paper, we solve this problem by developing a new piecewise Lyapunov function analysis and using parameter projection adaptation mechanism. Based on such approaches, an adaptive inverse compensation control scheme is designed to compensate for piecewise time-varying actuator backlash nonlinearity. It is proved that all signals of closed-loop system are ensured bounded. Moreover, the steady-state error is bounded by an adjustable scalar approaching to zero arbitrarily. Simulation also illustrates the obtained theoretical results.     

Extraction of Singular Points from Dense Motion Fields: An Analytic Approach

In this paper we propose a new method to extract the vortices, sources, and sinks from the dense motion field preliminary estimated between two images of a fluid video. This problem is essential in meteorology for instance to identify and track depressions or convective clouds in satellite images. The knowledge of such points allows in addition a compact representation of the flow which is very useful in both experimental and theoretical fluid mechanics. The method we propose here is based on an analytic representation of the flow. This approach has the advantage of being robust, simple, fast and requires few parameters.     

Path planning of a mobile robot by optimization and reinforcement learning

At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach, the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique

Compared with the probability approach, the non-probabilistic convex model only requires a small amount of samples to obtain the variation bounds of the imprecise parameters, and whereby makes the reliability analysis very convenient and economical. In this paper, we attempt to propose and create a correlation analysis technique mathematically for the non-probabilistic convex model, and based on it develop an effective method to construct the multidimensional ellipsoids on the uncertainty. A marginal convex model is defined to describe the variation range of each uncertain parameter, and a covariance is defined to represent the correlation degree of two uncertain parameters. For a multidimensional problem, the covariance matrix and correlation matrix can be created through all marginal convex models and covariances, based on which the required ellipsoid on the uncertainty can be conveniently achieved. By combining the correlation analysis technique and the reliability index approach, a non-probabilistic reliability analysis method is also developed for uncertain structures. Six numerical examples are presented to demonstrate the effectiveness of the present method.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

Sampled-data output feedback control of a class of uncertain nonlinear systems

In this paper, we study the problem of sampled-data output feedback control for a class of uncertain nonlinear systems. The main contributions of this work are two-fold: (i) we develop a systematic design procedure of sampled-data output feedback controller; and (ii) we provide a theoretical analysis of the sampled-data closed-loop system, which shows that the state of the plant can be regulated into any given neighborhood of the origin by an appropriate choice of design parameters and sampling period. Furthermore, the bound of the neighborhood is proportionally dependent on the sampling period. Finally, an illustrative example is given to demonstrate the effectiveness of the presented method.     

Exponential stabilization of inertial quaternion‐valued Cohen‐Grossberg neural networks: Lexicographical order method

In this article, a general class of delayed interval inertial Cohen‐Grossberg neural networks described by quaternion‐valued parameters is considered. Under the homeomorphism mapping theory and lexicographical order method, we investigate the exponential stabilization problem for the quaternion‐valued Cohen‐Grossberg neural networks. To do so, we verify the existence and uniqueness of the equilibrium point (EP), and then by designing a sampled‐data feedback controller, several sufficient criteria are derived to ascertain the robust stability of the EP for the given system. What should be mentioned is that the state parameters are taking values in an interval, which implies the states are taking values between two different quaternions, thus, a lexicographical order method is employed, which proposed an effective method to determine the “magnitude” of two different quaternions. Finally, numerical example is provided to demonstrate the effectiveness of the developed theoretical results.