复杂产品开发项目调度问题的模糊优化算法

对具有不确定时间参数的复杂产品开发项目调度问题,提出一种有效的模糊优化调度算法-基于预测的模糊BoP项目调度算法.首先,用模糊数表示不确定的时间参数,并构造相应的模糊数运算方法,对适合于确定性调度问题的BoP算法进行扩展,使其能处理模糊性时间参数.其次,修正了BoP算法中子项目调度方法,提高了算法的调度性能,降低了计算复杂度.大量的数值仿真实验表明,与基于启发式规则的调度算法相比,模糊BoP算法更适合于具有不确定时间参数的复杂产品开发项目调度问题.     

基于混沌时间序列的变电站设备温度预测

变电站输变线路和设备的温度变化能够反映其老化、负载过高等引起的安全隐患.通过对变电站设备温度数据的非线性分析和预测,实现对设备的有效预警,将避免事故引起的巨大损失.对变电站已测温度数据建立时间序列,利用小数据量法验证变电站设备温度时间序列的混沌特性.研究基于RBF神经网络的混沌时间序列预测并与神经网络预测进行对比,单步预测与多步预测结果均优于神经网络预测.仿真结论证明了基于神经网络的混沌时间序列预测方法的有效性.     

Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.     

A method for rapidly assessing and refining simple solvent treatments in molecular modelling. Example studies on the antigen-combining loop H2 from FAB fragment McPC603

Different simple solvent models have been implemented in anextended simulated annealing process (ESAP), developed by Higoet al. [(1992) Biopolymers, 32, 33–43] and proven to beable to predict ab initio the conformation of the antigen-combiningloop H2 from FAB McPC603. Hie rationale used here provides auseful new method for testing solvent models in general. Thedifferent solvent models comprise a high dielectric constant,a screened coulomb potential, a dummy water model and a statisticalcontinuum treatment of the solvent effect in which the reactionfield and the solvent accessible area is accounted for. To assessthe effect of the solvent, we tested the ability of simulationsto retain the experimental conformation of loop H2. We comparedthe different structures obtained at the end of the annealingprocess in terms of root mean square deviation (r.m.s.d.), forboth the backbone and for all atoms, root mean square (r.m.s.)fluctuation, solvent accessible surface area (ASA), hydrogenbonding network and - plot distribution. The relationship betweenthe r.m.s.d. and the internal energy of a structure is alsoevaluated in terms of precision and another possible methodfor obtaining the best conformation is discussed. The accuracyof modelling the coarse effect of the solvent and the similaritiesof the resulting structures with respect to the X-ray referencestructure are examined. The possible choice of one of thesesolvent models in the structure determination of an unknownloop structure is discussed     

机械结构相似度评价及可靠性预计研究

在用传统的相似产品法进行可靠性预计时,所选取的相似产品与评价对象之间差异较大,会造成预计结果的偏差较大。为解决这一问题,提出了一种基于"功能—运动—动作"结构化分解的可靠性预计新方法。通过明确产品的各大运动功能建立功能层,分析完成各功能的机械部件运动建立运动层,确定实现各机械部件运动的元动作运动单元建立元动作层,建立了可靠性预计的"功能—运动—元动作运动单元"层次模型。引入区间层次分析法,确定了"功能—运动—元动作运动单元"层次模型中各子代运动单元对父代运动单元的可靠性影响权重,并构建了由子代运动单元预计父代运动单元可靠性的数学模型。确定影响评价机械结构相似度的因素,构建了区间数综合评判云模型并选取与元动作运动单元相似的参考结构,以区间数综合评价云模型得出的相似度值修正参考结构的可靠性数据来预计元动作运动单元的可靠性。将元动作运动单元的可靠性预计值代入数学模型,层层预计父代运动单元的可靠性,从而得出整个产品的可靠性水平。最后以数控转台为例进行了分析,得出了可靠性预计值,并将该方法与传统方法进行了比较。结果表明,新的可靠性预计方法具有较好的可行性和较高的准确性。     

H.264帧内预测模式选择快速算法的研究

提出一种适用于H.264帧内预测的快速算法,利用相邻像素间的梯度筛选预测模式来避免不必要的预测模式计算。实验结果表明:用全I帧编码,该算法在图像质量和输出码率基本不变的情况下,编码时间大约节省了60%。     

On calibration error of randomized forecasting algorithms

It has been recently shown that calibration with an error less than Δ>0$\Delta >0$ is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are obtained by random rounding the deterministic forecasts up to Δ$\Delta$. We show that this error cannot be improved for a vast majority of sequences: we prove that, using a probabilistic algorithm, we can effectively generate with probability close to one a sequence “resistant” to any randomized rounding forecasting with an error much smaller than Δ$\Delta$. We also reformulate this result by means of a probabilistic game.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

基于运动方向预测的快速运动估计算法

利用序列图像的相邻块运动矢量的高度相关性和运动矢量的中心偏移特性,提出一种基于运动方向预测的快速运动估计算法。设计4种方向性模板,根据参考运动矢量预测出图像块的运动情况,根据不同的运动方向选择对应的方向性模板进行搜索。实验结果表明,该算法在速度和准确性方面都优于传统的快速运动估计算法。     

基于改进基因表达式程序设计的股票指数预测

介绍基因表达式程序设计方法的基本原理,针对股票指数分析与预测问题,在经典的GEP算法基础上,提出一种基于动态变异算子的改进的GEP算法——IGEP算法,动态变异算子随着进化代数和染色体所含基因数目不同而变化,从而加快了GEP的收敛速度和精确度,对算法进行了复杂度和收敛性分析。设计一种基于IGEP的股票指数分析与预测算法,数值实验结果表明,该算法优于经典GEP算法,具有较广泛的通用性。