关键词： Algorithm   Artificial neural network   Dominant protein function   Hidden markov model   Subfamily  

摘要： The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence.

关键词： Barvinok's algorithm   Rational generating function   Polytope  

摘要： We present a variant of Barvinok's algorithm for computing a short rational generating function for the integer points in a nonempty pointed polyhedron P := {x epsilon R-n : Ax <= b} given by rational inequalities. A main use of such a rational generating function is to count the number of integer points in P. Our variant is based on making an algebraic perturbation of the right-hand side b epsilon Q(m) of the inequalities, replacing each b(i), with b(i) + tau(i), where tau is considered to be an arbitrarily small positive real indeterminate. Hence, elements of our right-hand side vector become elements of the ordered ring Q[tau] of polynomials in tau. Denoting the algebraically-perturbed polyhedron as P(tau) subset of R[tau](n), we readily see that: (i) P(tau) is always full dimensional, (ii) P(tau) is always simple, and (iii) P(tau) contains the same integer points as P. Unlike other versions of Barvinok's algorithm, we will have to do some arithmetic in Q[tau]. However, because of (i) we will not need to preprocess our input polyhedron if it is not full dimensional, and because of (ii) we will not need to triangulate tangent cones at the vertices of the polyhedron. We give the details of our perturbation variant of Barvinok's algorithm, describe a proof-of-concept implementation developed in Mathematica, and present results of computational experiments. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Feature extraction  

摘要： With advances in remote sensing (RS) technology and platforms, more and more high-quality and fine spatial resolution satellite images are available. Manual method of feature extraction from remote sensing imagery is a tedious and time-consuming process. Thus automated and replicable technique plays vital role in updating lake database to evaluate the spatial and temporal evolution of lakes and ponds especially for vastly growing urban areas. This research work presents an artificial neural network (ANN) computed threshold value-based mathematical morphology (MM)-driven approach for extraction of lakes from satellite imageries with better accuracy. Accuracy of developed methodology has been assessed with the ground truths of the study area revealing better performance with different data-sets compared to existing methods. On an average scale for all data-sets used, the proposed algorithm is able to extract lakes with 99.47% accuracy and 0.9397 correlation coefficient (MCC). The existing classification method exhibited an accuracy of 98.75% and correlation coefficient of 0.89049. Similarly, the existing threshold-driven method has 99.31% accuracy and 0.90374 correlation coefficient. Maintenance of actual size and shape of the lakes, run-time control over structuring elements, semi-automation, faster processing, and single band adaptability are features of this work. © 2017 Indian Society for Hydraulics.

关键词： 分布式系统   燃料电池   自适应粒子群优化算法   经济性  

摘要： 目前,由多种可再生能源组成的分布式系统得到越来越广泛的关注。本文针对典型风光储系统进行了技术与经济性评估,该系统同时包含燃料电池作为稳定电源输出。本文基于自适应粒子群优化算法(Adaptive Particle Swarm Optimization,APSO),通过对风机,光伏,燃料电池及铅酸电池的数学建模,提出一种新型容量优化方法。采用传统迭代算法作为参考,同时考虑了可靠性与经济性因素,发现自适应粒子群优化算法在各方面表现优异。

关键词： 区间算法   遗传分析   全局优化  

摘要： 文章针对传统区间算法计算精度高,速度慢的特点,将遗传算法与区间算法的优势相互结合,提出了一种新型的混合区间遗传算法。该混合区间遗传算法利用区间算法的分支定界技术,保证了分支区间存在最优解的问题,为遗传搜索指定了准确界限,利用遗传算法确定全局最优解的上界限,减小了算法的计算量。通过实例研究证明,本文提出的混合区间遗传算法在保证计算精度的同时,简化了计算程序,提高了计算速度,能较好的解决复杂非线性函数中的全局优化问题。

关键词： 卫士通信息产业股份有限公司   密码   总裁   人生   安全   数学算法  

摘要： 它的出现总是伴随着一种神秘的色彩，深奥的数学算法，特殊的应用场景，对国家政治、军事、经济、社会方方面面不言而喻却又很难让人确切感知的重要意义，都让“密码”，这门古老而凝聚着人类智力精华的技艺，成为了一个让人充满好奇和敬畏的领域。

关键词： 计算机   算法   数学方法   应用   分析研究  

摘要： 随着现代社会计算机技术和信息技术的发展,计算机算法可以说是数学方法在现实生活中使用的表现之一,人们利用计算机进行大量编程的时候,是需要设计计算机算法,其主要是以数学方法作为参考的。并且随着教育改革的不断发展,在数学基础教育的时候也不断地加入了一些逻辑推理的方法和思想,因此本文在分析的过程中主要是对计算机算法中的数学方法的应用进行阐述,希望通过本文的分析可以为计算机算法的发展提供一些建议和借鉴。

关键词： 无速度传感器   电压矢量控制   数学模型   算法  

摘要： 无速度传感器电压矢量控制方式以无速度传感器矢量控制方式和空间电压矢量控制(SVPWM)方式的工作原理为理论依据,结合各种数学模型为支撑进行相应算法的实现.本文将介绍在实现无速度传感器电压矢量控制方式的算法推导时所要用到的几种数学模型.

关键词： CT重构   randon   变换   滤波反投影  

摘要： 2017高教社杯全国大学生数学建模竞赛A题是关于CT系统参数标定及成像模型。CT技术的核心是图像重建,在图像重建的过程中,建立基于radon变换的图像矩阵重建模型。图像重建的核心是重建算法,在迭代法和解析法中,解析法具有更严谨的数学知识基础,处理速度快。解析重建算法中,滤波反投影算法运算效率更高,图像重建的质量比较好,成本低。