Thermodynamics-inspired inverse halftoning viamultiple halftone images

Based on an analogy between thermodynamics and Bayesian inference,inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal(MPM) estimate.Applying Monte Carlo simulation to a set of snapshots of the Q-Ising model,it was demonstrated that optimal performance is achieved around the Bayes-optimal condition within statistical uncertainty and that the performance of the Bayes-optimal solution is superior to that of the maximum-a-posteriori(MAP) estimation which is a deterministic limit of the MPM estimate.These properties were qualitatively confirmed by the mean-field theory using an infinite-range model established in statistical mechanics.Additionally,a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation.Numerical simulations for a 256-grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately.     

水工钢闸门主梁模糊失效概率计算方法研究

为合理计算水工钢闸门主梁模糊失效概率,分别将主梁相对变形当作一个随机变量及三个变量的组合,采用积分法、当量随机化方法及蒙特卡罗法进行了计算。当相对变形为一个随机量时,采用积分法及当量随机化方法计算,两者的差别在于积分法用隶属函数描述模糊限值,当量随机化方法是将模糊限值当量作为一个随机量。当相对变形看作三个变量的组合时采用蒙特卡罗法进行计算,该方法考虑了三个变量的分布特性,更符合实际情况,模糊限值也用随机量表示。计算表明,积分法与当量随机化方法结果相近,验证了当量随机化方法的精度;蒙特卡罗法结果与相对变形服从正态分布时、用当量随机化方法计算的结果接近,故主梁相对变形服从正态分布更为合理。三种计算方法中,当量随机化方法计算失效概率相较于其它方法有计算过程简便,效率高的优点。     

曝光数据流优化问题的蒙特卡罗算法

集成工艺中控制图形发生器产生掩模图形的数据流文件的数据排列顺序优化,是一个其结点数可达数千个之多的大规模旅行商问题(从镜头架的位置,即每个矩形的矩心为结点),常规算法早已不适用。本文采用多阶段蒙特卡罗算法并加以改进,补充了“分层优化模式”,邻接结点的预合并和在各个阶段末提取共同子路径并删去相关结点等一系列逐步缩小问题规模的措施,形成了一个实用的优化算法。相应的软件已成功地应用于薄膜磁头的数据流文件的优化。     

A parallel Monte Carlo search algorithm for the conformational analysis of polypeptides

In recent years several approaches have been proposed to overcome the multiple-minima problem associated with nonlinear optimization techniques used in the analysis of molecular conformations. One such technique based on a parallel Monte Carlo search algorithm is analyzed. Experiments on the Intel iPSC/2 confirm that the attainable parallelism is limited by the underlying acceptance rate in the Monte Carlo search. It is proposed that optimal performance can be achieved in combination with vector processing. Tests on both the IBM 3090 and Intel iPSC/2-VX indicate that vector performance is related to molecule size and vector pipeline latency.     

基于主动学习Kriging模型的地下管线抗震可靠度分析

为提升地下承插式接口铸铁管线抗震可靠度的计算效率,提出基于主动学习Kriging代理模型的Monte Carlo模拟方法(AK-MCS)进行地下管线抗震可靠度计算。采用梁单元模拟管线结构,均布弹簧反映管土相互作用,接口弹簧模拟邻接管道约束作用,建立了地下管线地震响应分析模型;考虑管线接口允许位移、管线埋深、土体容重和内摩擦角等模型参数随机性的影响,以管线接口位移量为安全准则,采用主动学习Kriging模型方法建立管线接口位移响应与随机变量参数关系的代理模型,从而获得管线接口安全状态。算例结果表明,AK-MCS法计算得到的管线失效概率与传统Monte Carlo模拟计算的结果相对误差在5%以内,且AK-MCS法计算时间约为传统Monte Carlo模拟计算时间的2%。因此在进行管线可靠度计算时,主动学习Kriging代理模型方法具有准确性与高效性。     

焊接热影响区微观结构演化的蒙特卡罗计算机模拟方法

焊接热影响区是影响焊接性能的主要区域。焊接热影响区的晶粒长大常常导致焊接热影响区成为性能薄弱的环节，对焊接热影响区晶粒长大的研究一直是焊接接头的重要研究课题。焊接热影响区微观结构演化与诸多因素密切相关，是一个复杂的过程。运用适当的计算机方法进行模拟，完成对晶粒生长的完全预测，具有重要的意义，可以为优化焊接工艺参数，提高焊接质量提供依据。在此对在模拟焊接热影响区微观结构的演化时采用的基本方法进行综述。     

热成形过程微观组织模拟研究进展

金属在热成形过程中微观组织发生诸如晶粒长大、再结晶等一系列的复杂变化,会直接影响加工过程与产品的力学性能。对近年计算机技术在热成形微观组织演变模拟方面的研究进展做了比较全面的评述,比较了相关的微观组织模拟方法,分析了利用各种模拟技术所取得的研究成果。     

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.     

Molecular Dynamics Simulations Based on 1-Phenyl-4-Benzoyl-1-Hydro-Triazole ERRα Inverse Agonists

Estrogen-related receptor α (ERRα), which is overexpressed in a variety of cancers has been considered as an effective target for anticancer therapy. ERRα inverse agonists have been proven to effectively inhibit the migration and invasion of cancer cells. As few crystalline complexes have been reported, molecular dynamics (MD) simulations were carried out in this study to deepen the understanding of the interaction mechanism between inverse agonists and ERRα. The binding free energy was analyzed by the MM-GBSA method. The results show that the total binding free energy was positively correlated with the biological activity of an inverse agonist. The interaction of the inverse agonist with the hydrophobic interlayer composed of Phe328 and Phe495 had an important impact on the biological activity of inverse agonists, which was confirmed by the decomposition of energy on residues. As Glu331 flipped and formed a hydrogen bond with Arg372 in the MD simulation process, the formation of hydrogen bond interaction with Glu331 was not a necessary condition for the compound to act as an inverse agonist. These rules provide guidance for the design of new inverse agonists.     

The Effects of Random Porosities in Resonant Frequencies of Graphene Based on the Monte Carlo Stochastic Finite Element Model

With the distinguished properties in electronics, thermal conductivity, optical transparence and mechanics, graphene has a powerful potential in nanosensors, nano-resonators, supercapacitors, batteries, etc. The resonant frequency of graphene is an important factor in its application and working environment. However, the random dispersed porosities in graphene evidently change the lattice structure and destroy the integrity and geometrical periodicity. This paper focuses on the effects of random porosities in resonant frequencies of graphene. Monte Carlo simulation is applied to propagate the porosities in the finite element model of pristine graphene. The statistical results and probability density distribution of porous graphene with atomic vacancy defects are computed based on the Monte Carlo finite element model. The results of porous graphene with atomic vacancy defects are compared and discussed with the results of graphene with bond vacancy defects. The enhancement effects of atomic vacancy defects are confirmed in porous graphene. The influences of atomic vacancy defects on displacement and rotation vector sums of porous graphene are more concentrated in local places.