大规模网格模型间的快速视觉布尔运算

为了解决产品设计阶段中大规模网格模型间的布尔运算无法实现立等可得的速度瓶颈,提出了一种新算法。该算法利用离散化采样获得射线段点云模型,将三角面片间的3D布尔运算转换为射线段间的1D布尔运算,对相交处的交点进行高精度的求解和插值处理,使得布尔运算速度大为提高,从而大大提升复杂拓扑结构的产品设计效率。通过该算法所获得射线段点云模型可获得等同于基于三角网格的渲染效果,该方法可进行工程应用。     

Stability and stabilization of Boolean networks

The stability of Boolean networks and the stabilization of Boolean control networks are investigated. Using semi‐tensor product of matrices and the matrix expression of logic, the dynamics of a Boolean (control) network can be converted to a discrete time linear (bilinear) dynamics, called the algebraic form of the Boolean (control) network. Then the stability can be revealed by analyzing the transition matrix of the corresponding discrete time system. Main results consist of two parts: (i) Using logic coordinate transformation, the known sufficient condition based on incidence matrix has been improved. It can also be used in stabilizer design. (ii) Based on algebraic form, necessary and sufficient conditions for stability and stabilization, respectively, are obtained. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

基于遗传算法选优的集成手段与时序卷积网络的涡扇发动机剩余寿命预测

涡扇发动机作为航空航天领域的核心设备之一,其健康状况决定了航空器能否稳定可靠地运行。而对涡扇发动机的剩余寿命（RUL）进行判断,是设备监测与维护的重要一环。针对涡扇发动机监测过程中存在的工况复杂、监测数据多样、时间跨度长等特点,提出了一种遗传算法优选时序卷积网络（TCN）基模型的集成方法（GASEN-TCN）的涡扇发动机剩余寿命预测模型。首先,利用TCN捕获长跨度下的数据内在关系,从而对RUL作出预测;然后,应用GASEN集成多个独立的TCN,以增强模型的泛化性能;最后,在通用的商用模块化航空推进系统模拟模型（C-MAPSS）数据集上,对所提模型与当下流行的机器学习方法和其他的深度神经网络进行了比较。实验结果表明,在多种不同的运行模式和故障条件下,与流行的双向长短期记忆（Bi-LSTM）网络相比,所提模型都有着更高的预测准确率与更低的预测误差。以FD001数据集为例,在该数据集上所提模型的均方根误差（RMSE）相较Bi-LSTM低17.08%,相对准确率（Accuracy）相较Bi-LSTM高12.16%。所提模型在设备的智能检修与维护方面有着较好的应用前景。     

布尔网络的分析与控制——矩阵半张量积方法

布尔网络是描述基因调控网络的一个有力工具. 由于系统生物学的发展, 布尔网络的分析与控制成为生物学与系统控制学科的交叉热点. 本文综述作者用其原创的矩阵半张量积方法在布尔网络的分析与控制中得到的一系列结果. 内容包括: 布尔网络的拓扑结构, 布尔控制网络的能控、能观性与实现, 布尔网络的稳定性和布尔控制网络的镇定, 布尔控制网络的干扰解耦, 布尔 (控制) 网络的辨识,以及布尔网络的最优控制等.     

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.     

时域和酉空间中基于最大相关熵准则的非线性噪声处理

针对非线性噪声处理的问题,考虑到信号的高阶统计量以及在酉空间可以很好地处理非高斯噪声,提出了在时域和酉空间中基于最大相关熵准则（MCC）的噪声处理算法。结合MCC和梯度下降算法,设计出了时域中非线性噪声的滤波算法。同时将该算法推广到酉空间中噪声处理,给出了酉空间中基于MCC的滤波算法。通过仿真研究发现,在时域和酉空间中,基于MCC的滤波算法相对于传统的基于最小均方差（LMS）的滤波算法在处理非高斯噪声的问题时有着显著优势,以更快的收敛速度达到能够较完整地保留信号特征的效果。     

GPRS小区流量预测中时序模型的比较研究

针对通用无线分组业务(GPRS)小区流量预测问题,对几种典型时序预测模型的性能进行了综合分析。在总结时序预测模型使用步骤的基础上,分析了自回归（AR）、自回归移动平均（ARIMA）和乘积季节自回归求和移动平均（ARIMA）模型的性能。首先,对GPRS小区流量的变化情况进行分析;再根据流量的自相关系数和偏相关系数,从不同的角度进行分析,分别得到了流量变化的AR模型和ARMA模型;进而利用小区流量以天为周期变化的特点,得到了流量变化的乘积季节ARIMA模型。最后根据GPRS小区历史流量数据,应用这三种模型预测将来某一时间的流量,并对模型性能进行比较研究。     

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.     

基于多模型拟合的西安市生活垃圾量预测

大城市生活垃圾预测是政府环卫部门安全评价、规划和决策的基础。以西北最大城市西安为例,在分析现状基础上得出目前影响西安市生活垃圾产量的三个显著的驱动因子：西安市辖区建成区土地面积、城镇居民人均可支配收入和市区户籍人口。先建立GM（1,1）灰色预测模型和最小二乘法多项式拟合人均可支配收入增长模型,对三个影响因素指标分别进行预测;后建立多元线性回归模型,结合前期三个指标预测值,预测2011-2020年西安市生活垃圾产生量。显著性F检验表明多模型拟合预测合理,预测结果有意义。     

瑞利衰落信道改进模型及性能分析

自回归(AR)模型和Clarke模型(CLARKE R H. A statistical theory of mobile-radio reception. Bell System Technology Journal, 1968, 47(6): 957-1000)常用于产生瑞利(Rayleigh)衰落信道。但是仿真结果显示自回归模型有其自身无法避免的数值缺陷,因此无法产生理想的Rayleigh衰落信道特性。在Clarke模型的基础上提出了改进模型。新模型合理地减少了入射波到达角的随机分布范围,在保证到达角随机分布特性的同时获得了信道模型更好的统计特性。仿真结果表明,改进模型比Clarke模型具有更好的统计性能,可以用于产生较理想的Rayleigh衰落信道。     

TV改进算法及其在人脸识别中的应用

针对传统全变分（TV）模型在测试过程中存在的问题,将弹性网引入TV模型中,采用二次多项式对TV模型所丢弃的人脸低频信息进行光照归一化处理,并提取图像的高频信息,在YaleB图像库中测试其性能,仿真实验结果表明,相对于TV模型,TV＋二次多项式模型能够有效提高图像识别率。     

基于时间注意力机制和EfficientNet的视频暴力行为检测

针对一般的暴力行为检测方法模型参数量大、计算复杂度高、准确率较低等问题,提出一种基于时间注意力机制和EfficientNet的视频暴力行为检测方法。首先将通过对数据集进行预处理计算得到的前景图输入到网络模型中提取视频特征,同时利用轻量化EfficientNet提取前景图中的帧级空间暴力特征,并利用卷积长短时记忆网络（ConvLSTM）进一步提取视频序列的全局时空特征;接着,结合时间注意力机制,计算得到视频级特征表示;最后将视频级特征表示映射到分类空间,并利用Softmax分类器进行视频暴力行为分类并输出检测结果,实现视频的暴力行为检测。实验结果表明,该方法能够减少模型参数量,降低计算复杂度,在有限的资源下提高暴力行为检测准确率,提升模型的综合性能。     

利用CPU时间戳实现局域网时间同步的新机制

当前在网络应用中,经常需要多台计算机协同工作,因而要有一个统一时钟。提出一种利用CPU内部高精度时间戳实现局域网时钟同步的新机制,并用VC++语言编写了服务端与客户端软件,且在Windows XP 和Windows 2000环境的小型局域网进行测试。结果表明,该算法同步精度较高,且系统运行稳定。     

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.     

Scalability analysis of a two level domain decomposition approach in space and time solving data assimilation models

We are concerned with the mapping on high performance hybrid architectures of a parallel software implementing a two level overlapping domain decomposition, that is, along space and time directions, of the four dimensional variational data assimilation model. The reference architecture belongs to the SCoPE (Sistema Cooperativo Per Elaborazioni scientifiche multidisciplinari) data center, located at University of Naples Federico II. We consider the initial boundary problem of the shallow water equation and analyse both strong and weak scaling. Keeping the efficiency always greater than $$ and about $$ in most cases, we experimentally find that the isoefficiency function grows a little more than linearly with respect to the number of processes. Results, obtained by using the parallel computing toolbox of MATLABR2013a, are in agreement with the algorithm's performance prevision based on the scale up factor, confirming the appropriate mapping of the algorithm on the hybrid architecture.     

Synthetic detection of change point and outliers in bilinear time series models

This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective.     

MBMS中模型的关系表达和自动复合研究

由于决策环境的变化及决策范围的扩展,DSS中模型不断被分批开发和增加,同时决策问题的复杂性往往导致模型的多样性,由此模型关系的表达和管理往往比较复杂,模型间的冗余及整体关系难于掌握。使用扩展的Petri网表达MBMS中各模型的相互关系,论述了PN自动成图算法,增强决策者对整个MBMS的整体掌握程度,有利于发现模型间的求解冲突与冗余;同时,通过从决策目标出发,通过基于PN的逆向推导,发现决策问题求解路径,降低了DSS应用门槛。