一种基于边界特征线且特征点可变的二阶非线性离散跟踪微分器及在测速定位系统中的应用

利用状态反推方法确定最速离散二阶系统的线性区域的边界特征线及控制特征线,以相平面上的点及边界曲线、控制线的相对位置按线性规则确定控制量的大小,区分可达区与线性区,并依此构造最速分段线性函数形式的跟踪微分器（Tracking-differentiator,TD）,这种算法可以方便地修改特征点,适应能力强,而且运算中不包含任何根号运算,使得控制综合函数的形式极大简化,有利于工程实现.对正弦信号及方波信号的仿真结果表明了上述结论的合理性.验证了特征点分段线性算法在适当修改特征点后得到的TD,与经典TD以及它的线性近似进行比较,跟踪能力和微分提取能力都得到了较大的提高.应用移动平均算法构成的跟踪微分器组,具有相位补偿功能,适当选取TD参数后,得到了滤波能力强、相位特性良好的滤波器,并应用于基于长定子齿槽检测的永磁电动磁悬浮列车的测速定位系统中.实验结果表明,本文提出的简化TD按相位补偿确定的方案能有效滤除脉冲和扰动等噪声,对过轨道接缝时的畸变信号进行修复,边界容易修改,算法简单有效,实时性强,易于工程实现.     

广义Mamdani模糊系统依K-积分模的泛逼近及其实现过程

首先,通过引入拟减法算子给出K-积分模定义,并针对广义Mamdani模糊系统实施等距剖分其输入空间. 其次,应用分片线性函数（Piecewise linear function,PLF）的性质构造性地证明了广义Mamdani模糊系统在K-积分模意义下具有泛逼近性,从而将该模糊系统对连续函数空间的逼近能力扩展到一类可积函数类空间上. 最后,通过模拟实例给出该广义Mamdani模糊系统对给定可积函数的泛逼近及实现过程.     

氯碱整流新技术应用动态

概述了整流系统的配置，全数字化的计算机控制和监测系统，整流装置的电源和负载模式，整流辅助设备的新产品，新技术以及谐波抑制措施等新技术在工业上的应用现状。     

Rheological properties and compatibility of NR/EPDM and NR/brominated EPDM blends

Ethylene–propylene–diene terpolymer (EPDM) was modified by bromination reaction. Blending the resulting brominated EPDM with natural rubber (STR5L) and blending the unmodified EPDM with STR5L at various compositions were carried out. The rheological properties of the blends were investigated using a capillary extrusion. Shear flow curves of the pure rubbers and their blends illustrated the pseudoplastic property as shear thinning behavior with a power law index n < 1. True shear viscosity of all blends showed the negative deviation in relation to their additive values. Rheological behavior and two T_g's found from the DSC thermograms at all blend compositions indicated blend incompatibility for both sets of blends. The incompatibility of the vulcanized blends was also found by measuring the spin–spin relaxation time T₂ by pulsed NMR. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 837–847, 2003     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

Teeth segmentation of dental periapical radiographs based on local singularity analysis

Teeth segmentation for periapical raidographs is one of the most critical tasks for effective periapical lesion or periodontitis detection, as both types of anomalies usually occur around tooth boundaries and dental radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in periapical radiographs. The method consists of four stages: image enhancement using adaptive power law transformation, local singularity analysis using Hölder exponent, tooth recognition using Otsu's thresholding and connected component analysis, and tooth delineation using snake boundary tracking and morphological operations. Experimental results of 28 periapical radiographs containing 106 teeth in total and 75 useful for dental examination demonstrate that 105 teeth are successfully isolated and segmented, and the overall mean segmentation accuracy of all 75 useful teeth in terms of (TP, FP) is (0.8959, 0.0093) with standard deviation (0.0737, 0.0096), respectively.     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

Time Dependence and Moments of a Family of Time-Varying Parameter Garch in Mean Models

Abstract.  In this paper we consider the time series dependence, stationarity, and higher moments issues of a family of first-order conditionally heteroskedastic in mean models with a possibly time-varying mean parameter. The interest in these models lies in the fact that economic theory and physics often require the connection between the first and second conditional moments of time series. Our results reveal important properties of these models, which are consistent with stylized facts in financial and turbulence data sets. They can also be employed for model identification, estimation, and testing.     

Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Multi-Agent Systems (MASs) have long been modeled through knowledge and social commitments independently. In this paper, we present a new method that merges the two concepts to model and verify MASs in the presence of uncertainty. To express knowledge and social commitments simultaneously in uncertain settings, we define a new multi-modal logic called Probabilistic Computation Tree Logic of Knowledge and Commitments (PCTL^kc in short) which combines two existing probabilistic logics namely, probabilistic logic of knowledge PCTLK and probabilistic logic of commitments PCTLC. To model stochastic MASs, we present a new version of interpreted systems that captures the probabilistic behavior and accounts for the communication between interacting components. Then, we introduce a new probabilistic model checking procedure to check the compliance of target systems against some desirable properties written in PCTL^kc and report the obtained verification results. Our proposed model checking technique is reduction-based and consists in transforming the problem of model checking PCTL^kc into the problem of model checking a well established logic, namely PCTL. So doing provides us with the privilege of re-using the PRISM model checker to implement the proposed model checking approach. Finally, we demonstrate the effectiveness of our approach by presenting a real case study. This framework can be considered as a step forward towards closing the gap of capturing interactions between knowledge and social commitments in stochastic agent-based systems.