基于改进鲸鱼优化算法的近红外光谱波长变量选择方法及其应用

该文在群体智能的鲸鱼优化算法（WOA）基础上,提出了一种改进的鲸鱼优化算法（iWOA）用于近红外光谱波长的选择。首先引入混沌策略初始化种群,避免算法过早陷入局部最优;其次引入一种非线性时变Sigmoid传递函数和贪心算法思想,提升算法探优能力,使得模型获得更好的预测精度。为验证算法的有效性,以玉米脂肪、蛋白质、淀粉、水4个指标的近红外光谱数据进行偏最小二乘（PLS）建模分析,并与其他算法进行对比。结果表明,iWOA算法能在最短时间内,有效地筛选出波长变量,降低模型的复杂度,提升模型的预测精度。在玉米脂肪、蛋白质、淀粉、水含量的预测上,与全光谱相比,模型的预测集均方根误差（RMSEP）分别从0.077 2、0.122 4、0.334 4、0.059 5降至0.033 2、0.050 7、0.139 2、0.004 4,预测精度分别提升了57.0%、58.6%、58.3%、92.6%;算法选出的波长数目分别为：84、69、87、66。     

介观生化反应的随机和混合模拟算法

随着细胞等介观体系中研究的深入,介观生化反应的模拟方法日益引起人们的关注。一方面,这类体系中涨落效应显著,化学反应本质上是随机的离散过程,经典的确定性模拟方法不再适用,需要使用随机模拟方法;另一方面,这种体系中子反应的速率和反应物的分子数目可能有显著差别,存在多尺度特性,使一般随机算法的模拟效率极低,对反应的准确高效模拟提出了新的要求。本文简述了Gillespie提出的一系列基本随机算法,综述了近年来发展的针对反应多尺度特性的改进随机算法和混合算法,介绍了各类算法的特点、实现的技术问题及相对其他算法和实际生物信号描述表现出的优缺点。     

基于分通道自适应匹配的梯度保持色彩迁移算法

本文针对经典Welsh与Reinhard算法进行改进，提出一种基于分通道自适应匹配的梯度保持色彩迁移算法。引入L、a、b三通道作为匹配依据，因通道间的序列不相关，对单一通道进行处理时不会影响到另外两通道，消除了RGB颜色模式中单一目标区域易受多通道影响的弊端；对整体灰度值相近的图像，将色度均值差异最大的待处理区与背景区通道及参考图像对应通道进行自适应匹配，解决了Welsh算法对灰度差异小或灰度范围重叠的彩色源图像分级易造成误判的问题。然后引入梯度因子，同参考图像与源图像标准差比值作加权运算，将结果作为本文改进迁移算法的缩放比例系数，对匹配到的每组像素采用保持梯度的色彩迁移算法进行着色，避免了Reinhard算法易出现色彩细节信息丢失、阶调间过渡不自然等问题。实验证明，本文改进算法所得目标图像的彩色化效果均优于经典Welsh与Reinhard算法，极大提升了色彩迁移算法的灵活性与适用性。     

三维人脸识别算法研究

近年来,基于三维图像的人脸识别技术已经取得了很大进展,在约束环境下也能获得很好的识别性能,但仍受限于姿态、表情等因素,需要从算法上改进才能解决其影响。本文分别从基于空域直接匹配、基于局部特征匹配和基于整体特征匹配3个角度出发,对人脸匹配算法以及融合算法进行了研究,列出了部分改进算法的实验结果,并分析了算法有效性的原因,总结了目前面临的三维人脸识别算法难以突破的一些困难及未来的研究趋势。     

神经网络反传改进算法及其用于分析复方药物

提出了一种反向传播改进算法ＭＢＰ，能加快神经网络ＮＮ的收敛速度，增强反传算法的实用性。用于分析多元复方药物，结果良好，操作简便。     

数字图像相关方法中基于改进IC-GN算法高精度形变测量研究

在数字图像相关方法对物体形变测量中,FA-NR算法实现了高精度测量,IC-GN算法在此基础上提高了测量效率。为进一步提升测量精度,提出了一种基于IC-GN的改进算法(GIC-GN)。在已知整像素初始位置上,通过梯度法求得更准确的亚像素位移,减小Hessian矩阵计算过程中产生的误差,同时加快迭代的收敛速度,有效提高了测量精度和效率。仿真实验验证结果表明,GIC-GN算法误差能够稳定在10^-4～10^-3 pixel之间,对比IC-GN算法精度提升了10%～60%,耗时是FA-NR算法的0.1倍、IC-GN算法的0.8倍,能够实现对物体形变信息的高精度、高效率测量。     

农药残留快速检测中酶催化动力学分析的算法改进

本文对现有的农药残留快速检测中酶催化动力学分析法的算法进行了改进,以多点采样最小二乘法代替简单的两点法计算斜率,以光强值代替吸光度值来测量斜率。这些改进简化了计算,提高了分析结果的准确度,并在本研究所开发的便携式农残检测仪上得到了应用。     

一种新的目标转换因子分析算法用于混合物红外光谱中纯组分光谱的解析

本文针对运用目标转换因子分析从混合物红外光谱中解析出纯组分光谱的有关限制,进行了有效的改进。把抽象正交特征光谱与以单一向量为初始向量的目标转换因子分析相结合,提出了一种新的算法,结果令人满意。     

多组分多出口稀土串级萃取静态优化设计研究(I)静态设计算法

研究了多组分、多出口稀土串级萃取体系静态优化设计过程的精确计算方法. 首先推导出串级萃取体系最重要的物料平衡和萃取平衡关系, 其次根据分离要求等条件确定了各出口的相对流量, 利用萃取平衡和物料平衡关系进行静态递推, 并在求解过程中根据物料平衡关系引入了校正参数, 通过对递推结果的迭代校正, 最终得到收敛的计算结果, 解决了静态设计算法的关键问题. 这一方法为多组分多出口稀土串级萃取过程的静态优化设计提供了精确算法.     

一种新的基于多重液相色谱-质谱实验肽信号峰形相似性的校准算法

提出了一种基于多次重复液相色谱-质谱(LC-MS)实验,结合肽信号时间差校准的峰形相似性统计学习模型,解决了重复实验数据肽链校准匹配准确性与覆盖率低的问题。采用统计学习的方法,首先建立时间差统计模型,结果表明仅靠时间特征无法完全消除校准误差。因此,除了时间特征,引入了峰形相似性特征,即认为同一种肽链在多次重复实验谱图中会产生相似的LC峰形。通过选取训练数据集,提出了一种新的基于LC峰形相似性的肽信号校准算法,并通过测试序列完成模型测试。结果表明,改进算法的准确率达98. 3%;将该数学模型应用于校准匹配两个实验数据的所有LC-MS/MS肽链,其覆盖率达91. 0%。峰形相似性特征结合时间特征可以提升多次重复LC-MS实验中相关肽链信号的匹配校准的准确性与覆盖率。     

Design of new selective inhibitors of cyclooxygenase-2 by dynamic assembly of molecular building blocks

A method of dynamically assembling molecular building blocks - DycoBlock - has been proposed and tested by Liu et al. This method is based on multiple-copy stochastic dynamics simulation in the presence of a receptor molecule. In this method, a novel algorithm was used to dynamically assemble the molecular building blocks to form candidate compounds. Currently, some new improvements have been incorporated into DycoBlock to make it more efficient. In the new version of DycoBlock, the binding energy and solvent accessible surface area (SASA) can be used to screen the resulting compounds. A simple clustering algorithm based on molecular similarity was developed and used to classify the remaining compounds. The revised DycoBlock was tested by breaking SC-558 - a selective inhibitor of cyclooxygenase-2 (COX-2) - into building blocks and reassembling them in the active site of the enzyme. The accuracy of recovery grew to 58.8% while it was only 16.7% in the previous version. Then, thirty-three kinds of molecular building blocks were used in the design of novel inhibitors and the investigation of diversity. As a result, a total of 1441 compounds was generated with high diversity. After the first screening procedure, there remained 864 reasonable compounds. The results from clustering indicate that the structural motifs in the diarylheterocycle class of COX-2-selective inhibitors have been generated using the revised DycoBlock, and their binding modes were investigated.     

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.     

FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm

Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50 % than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.     

基于周期调节的随机共振算法对化学弱信号的检测研究

提出了一种新的随机共振算法,通过对系统的周期进行调节以实现系统的共振.讨论了实现共振的系统参数,并对模拟信号和拉曼光谱实验信号进行了处理.结果表明,该方法不仅可方便地用于弱化学信号的处理,克服检出信号的滞后现象,而且对不同噪声水平的信号均有良好的适用性.     

New method for parallel computation of Hessian matrix of conformational energy function in internal coordinates

A new algorithm for parallel calculation of the second derivatives (Hessian) of the conformational energy function of biomolecules in internal coordinates is proposed. The basic scheme of this algorithm is the division of the entire calculation of the Hessian matrix (called "task") into subtasks and the optimization of the assignment of processors to each subtask by considering both the load balancing and reduction of the communication cost. A genetic algorithm is used for this optimization considering the dependencies between subtasks. We applied this method to a glutaminyl transfer RNA (Gln-tRNA) molecule for which the scalability of our previously developed parallel algorithm was significantly decreased when the large number of processors was used. The speedup for the calculation was 32.6 times with 60 processors, which is considerably better than the speedup for our previously reported parallel algorithm. The elapsed time for the calculation of subtasks, data sending, and data receiving was analyzed, and the effect of the optimization using the genetic algorithm is discussed.     

A quasi-Newton algorithm for first-order saddle-point location

Summary A new algorithm for the location of a transition-state structure on an energy hypersurface is proposed. The method is compared to three other quasi-Newton step calculations available in literature. Numerical results derived from several examples are compared to those obtained by the two algorithms implemented in the Gaussian package.     

免疫-遗传算法用于混合物重叠核磁共振信号解析

通过对免疫系统中抗体对外来抗原的识别、消除等过程的模拟,建立了一种新型的免疫算法模型.将标样信号作为抗体,混合物重叠信号作为抗原输入免疫算法模型,通过迭代运算,从抗原中消除抗体所表示的信息,当抗原被抗体完全消除时,即实现了混合物重叠信号的解析.对多组分混合氨基酸NMR谱图的解析结果证明,该算法可方便地用于多组分重叠信号的解析,为利用数据库解析混合物或生物大分子等物质的复杂NMR谱图开辟了一条全新的途径.     

Classification of Edible Oils by Infrared Spectroscopy with Optimized K-Means Clustering by a Hybrid Particle Swarm Algorithm

The K-means algorithm has some limitations including dead-unit properties, heavy dependence on the initial choice of cluster centers, convergence to local optima, and sensitivity to the number of clusters. This paper presents an efficient algorithm that optimizes K-means clustering by a hybrid particle swarm algorithm. The modified discrete algorithm is used to select variables and is continuously applied to update cluster centers simultaneously. The nearest center classification is then employed to classify the test samples. The proposed algorithm was applied to discriminate various edible oil varieties by employing Fourier transform infrared spectroscopy. As a comparison, the common K-means clustering, principal component analysis, and partial least squares techniques were also applied to classify these edible oil samples. Results demonstrated that the proposed method is an accurate and rapid strategy for identifying edible oils.     

Asymmetric least squares for multiple spectra baseline correction

In this paper, based on asymmetric least squares smoothing, a new algorithm for multiple spectra baseline correction is proposed. By means of the similarity among the multiple spectra, the algorithm estimates the baselines by penalizing the differences in the baseline corrected signals, which makes the algorithm possible to eliminate scatter effects on the spectra. In addition, a relaxation factor which measures the similarity of the baseline corrected spectra is incorporated into the optimization model and an alternate iteration strategy is used to solve the optimization problem. The proposed algorithm is fast and can output multiple baselines simultaneously. Experimental results on both simulated data and real data demonstrate the effectiveness and efficiency of the algorithm.