Partial stability and stabilisation of Boolean networks

In this paper, we investigate the stability of Boolean networks and the stabilisation of Boolean control networks with respect to part of the system's states. First, an algebraic expression of the Boolean (control) network is derived by the semi-tensor product of matrices. Then, some necessary and sufficient conditions for partial stability of Boolean networks are given. Finally, the stabilisation of Boolean control networks by a free control sequence and a state-feedback control is investigated and the respective necessary and sufficient conditions are obtained. Examples are provided to illustrate the efficiency of the obtained results.     

Boolean problem of sequential minimization of moduli of linear functions and stability theorems

A multicriterion Boolean problem of lexicographic optimization with partial criteria represented by moduli (absolute values) of linear functions is considered. Five types of stability of a set of lexicographic optima under “small” variations in the parameters of a vector criterion are investigated. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 178–187, September–October 2007.     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Clustering analysis of temporal gene expression data is widely used to study dynamic biological systems, such as identifying sets of genes that are regulated by the same mechanism. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. In this paper, we introduce an improved clustering approach based on the regularized spline regression and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal number of basis and the regularization parameter. We show that this treatment can help to avoid over-fitting. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure for clustering. The energy based measure can include the temporal information and relative changes of the time series using the first and second derivatives of the time series. We demonstrate that our method is robust to noise and can produce meaningful clustering results.     

Algebraic analysis of the generating functional for discrete random sets and statistical inference for intensity in the discrete Boolean random-set model

We consider binary digital images as realizations of a uniformly bounded discrete random set, a mathematical object that can be defined directly on a finite lattice. In this setting we show that it is possible to move between two equivalent probabilistic model specifications. We formulate a restricted version of the discrete-case analog of a Boolean random-set model, obtain its probability mass function, and use some methods of morphological image analysis to derive tools for its statistical inference.This research was partially supported by National Science Foundation grant NSFD CDR 8803012 through the Engineering Research Centers program.     

Optimal control of (bio-)chemical reactors: generic properties of time and space dependent optimization

This paper deals with the optimal control (OC) in the space domain of (bio-)chemical reactors. As an example, the determination of optimal heat exchanger temperature profiles of exothermic tubular reactors under the assumption of steady-state and plug flow characteristics is investigated. To enable a trade-off between process performance and energy loss a weighted cost criterion is defined. Application of the Minimum Principle of Pontryagin leads to extremal control profile structures which are very similar to those obtained during optimization (in the time domain) of well mixed bioreactors. The latter problem has been analyzed in detail over the last two decades. The analogy is reflected at various levels during analytical optimal control computations.     

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Due to the complicated structure of breast and poor quality of ultrasound images, accurately and automatically locating regions of interest (ROIs) and segmenting tumors are challenging problems for breast ultrasound (BUS) computer-aided diagnosis systems. In this paper, we propose a fully automatic BUS image segmentation approach for performing accurate and robust ROI generation, and tumor segmentation. In the ROI generation step, the proposed adaptive reference point (RP) generation algorithm can produce the RPs automatically based on the breast anatomy; and the multipath search algorithm generates the seeds accurately and fast. In the tumor segmentation step, we propose a segmentation framework in which the cost function is defined in terms of tumor?s boundary and region information in both frequency and space domains. First, the frequency constraint is built based on the newly proposed edge detector which is invariant to contrast and brightness; and then the tumor pose, position and intensity distribution are modeled to constrain the segmentation in the spatial domain. The well-designed cost function is graph-representable and its global optimum can be found. The proposed fully automatic segmentation method is applied to a BUS database with 184 cases (93 benign and 91 malignant), and the performance is evaluated by the area and boundary error metrics. Compared with the newly published fully automatic method, the proposed method is more accurate and robust in segmenting BUS images.     

人工鱼群算法在基因调控网络中的应用研究

在分析基因调控网络现状及优缺点的基础上,提出利用人工鱼群算法对阈值布尔网络模型构建下的基因调控网络进行研究。将阈值布尔网络模型应用于花发育形态模型,构建基于预定义吸引子和极限环的综合网络。比较人工鱼群算法与模拟退火算法在基因调控网络中的应用情况,分析网络节点更新机制变化时布尔网络保留吸引子的能力,发现在极限环长度为2和特定网络拓扑下网络才具有鲁棒性。实验结果表明,与模拟退火算法相比,人工鱼群算法在网络发现、鲁棒性方面具有更好的性能,因此利用人工鱼群算法学习布尔网络结构是有效可行的。     

Reconstruction of missing data in multidimensional time series by fuzzy similarity

The present work addresses the problem of missing data in multidimensional time series such as those collected during operational transients in industrial plants. We propose a novel method for missing data reconstruction based on three main steps: (1) computing a fuzzy similarity measure between a segment of the time series containing the missing data and segments of reference time series; (2) assigning a weight to each reference segment; (3) reconstructing the missing values as a weighted average of the reference segments. The performance of the proposed method is compared with that of an Auto Associative Kernel Regression (AAKR) method on an artificial case study and a real industrial application regarding shut-down transients of a Nuclear Power Plant (NPP) turbine.     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

An experiment for improving students performance in secondary and tertiary education by means of m-learning auto-assessment

Mobile learning is considered an evolution of e-learning that embraces the ubiquitous nature of current computational systems in order to improve teaching and learning. Within this context it is possible to develop mobile applications oriented to learning, but it is also important to assess to what extent such applications actually work. In this paper we present a new tool designed to reinforce students’ knowledge by means of self-assessment. Improvement in student achievement was evaluated and an attitudinal survey was also carried out to measure student attitudes towards this new tool. Three different experimental groups were selected for this research, with students aged from 14 to 21 years old, including high-school and university students. Results show that this kind of tool improves student achievement, especially amongst younger learners, with a relatively low impact on current teaching activities and methodology.     

PEMFC的PSO优化LS-SVM动态建模仿真

质子交换膜燃料电池(PEMFC)作为一种电化学反应装置,湿度管理对于它的运行性能十分关键.为了建立相应的动态模型,采用了最小二乘支撑向量机(LS-SVM)算法,并使用微粒群算法(PSO)对LS-SVM模型参数进行了自动优化.这种方法建立的动态数学模型能够以进气湿度和工作电压为输入量预测输出的电流密度.仿真结果显示,模型的预测值和实验数据间的平均平方相对误差小于0.0001,能够达到很好的预测精度.因此,这种优化建模策略可以有效的应用到PEMFC研究中.     

组件化模具PDM系统的组件结构与规划

对组件进行系统合理地规划是基于组件技术开发PDM系统的PDM系统提出了更高的要求.为此,从系统规划的角度提出了组件化模具PDM系统的组件层次体系结构,对系统组件进行了详细地划分,最后引入GenVoca模型对组件间关系进行形式化描述,保证了组件之间逻辑关系的正确性.     

Simultaneous compensation of hysteresis and creep in a single piezoelectric actuator by open-loop control for quasi-static space active optics applications

Owing to their excellent properties piezoelectric actuators are studied as embedded elements for the quasi-statically active shape control of spatial optical mirrors. However, unwanted nonlinear effects in piezoelectric actuators, i.e., hysteresis and creep, severely limit their performance. This paper aims at developing a control methodology to compensate hysteresis and creep in a piezoelectric actuator simultaneously for quasi-static space active applications. In the methodology developed, hysteresis and creep behaviors are successively compensated by open-loop control. First, a derivative Preisach model is proposed to accurately portray the hysteresis while requiring relatively few measurements and describing the detachment between major and minor loops. The inverse derivative Preisach model is derived and inserted in open-loop to achieve hysteresis compensation. Then, the creep in the hysteresis compensated piezoelectric actuator is described by the use of a nonlinear viscoelastic model and a low pass filter is suggested to eliminate the effect of the inverse derivative Preisach model on the step reference input. To invert the creep model, the concept of “input relaxation” is implemented and an inverse multiplicative structure allows identifying the parameters of the inverse model while circumventing the difficulty of a mathematical computation. Finally, by cascading the low pass filter, the inverse model of creep and the inverse derivative Preisach model one after the other with the single piezoelectric actuator, the simultaneous compensation of hysteresis and creep is achieved. Experimental results show that in the case of step-like reference signals the hysteresis and the creep in a piezoelectric actuator can be significantly reduced at the same time. It implies that the developed methodology is effective and feasible in space active optics applications for which quasi-static distortions need to be compensated.     

高速燃油离心泵内流场分析和气蚀过程仿真

采用混合两相流模型和气蚀模型,采用两种网格技术多参考系模型和滑移网格模型对某型航空发动机高速燃油加力离心泵内流场和气蚀过程进行了二维数值仿真.首先使用多参考系模型得到稳态非气蚀流场,然后作为初始条件引入滑移网格技术同时加人气蚀模型对气蚀全过程进行了瞬态仿真.不仅良好地模拟出泵腔内空泡初生、发展、消失的瞬态全过程,同时发现了稳定的瞬态压力场和气泡体积分数场的周期性发展规律,并揭示这一规律由排出管引起.详细分析了气蚀易发生位置的空间分布,排出管对气蚀发生的影响,高度空化流中蜗室出口壁面处气蚀破坏及其成因.仿真结果与实物气蚀破坏情况符合良好,对认识高速离心泵内燃油流动,预测并克服气蚀发生有指导意义.     

激发-发射矩阵荧光结合二阶校正法测定人体血浆样和牛蒡子药材中的牛蒡苷含量

本文利用激发-发射矩阵荧光与二阶校正算法相结合,实现快速测定人体血浆样和牛蒡子对照药材中的牛蒡苷含量.采用二阶校正方法的平行因子分析(PARAFAC)和交替归一加权残差(ANWE)两种算法进行解析,所得血浆基体干扰下牛蒡苷的回收率分别为(98.7±1.4)%和(99.7±1.6)%,实现了对血浆样中的牛蒡苷含茸的直接快速定量测定.此外,用上述两种方法对牛蒡子药材中的牛蒡苷含量进行快速测定,所得结果与液相色谱方法结果进行比较,结果令人满意.实验结果表明,此方法方便快捷,可用于复杂试样中未知干扰共存下牛蒡苷含量的测定.     

浓相高密度气固流化床动力学特性模拟研究

气固流化床内部运动状态和流体力学特性是研究者普遍关心的问题。浓相高密气固流化床就有不同于一般化学工业流化床的特点：固相介质密度高（4．0—5．0g／cm^3），空隙率小（0．3～0．42），气相速度小（0．15—0．30m／s）。使用基于物质、质量守恒定律建立了双流体模型对气固流化床动力学特性进行了模拟研究。在二维直角坐标系中用控制容积差分法离散方程。并在气相表观速度为0．157m／s条件下，基于0．1cmm＊0．1cm的交错网格，求解了时间长度为1．2秒的固体介质流动状态。计算结果表明：固相的流动形态为中心上升边界下降的趋势。该结果与试验结果符合的很好。     

专题搜索引擎中信息过滤的研究与实现

在对经典信息过滤算法进行研究分析的基础上,结合网页重要性和主题相关性,给出了专题搜索引擎中信息过滤模块的设计思路和实施策略,并在专业词汇、查询扩展和用户反馈等方面对算法进行了改进和拓展.最后给出了系统的实施方案,并对将来的工作做了展望.     

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy).At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation.At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT_canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level (). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT_canopy and was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT_canopy and an overestimation of leaf with relative errors around 19%.Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.