基于参数智能化采集的盾构管片自动选型算法研究

为解决人工管片选型给盾构姿态和成型管片质量带来的隐患,将成熟的人工管片选型经验逻辑和合理简化计算模型根植于管片自动选型决策算法的内核,以影响管片选型的施工参数为研究对象,提出综合考虑盾尾间隙、推进油缸行程差和盾构趋势的管片选型计算方法和决策算法,预先考虑各影响因素实际施工中所有可能出现的取值范围以及不同施工工况下的权重变化。该软件首次针对大直径盾构特点开发8组盾尾间隙和6组油缸行程的算法,借助盾尾间隙智能化测量和监控屏幕参数图像识别技术,实现通用型管片选型参数智能化采集。在盾构隧道施工中配套使用,经多个项目现场验证和应用,管片选型算法合理,契合现场施工要求,起到规范或代替人工管片选型施工、保障成型隧道质量和盾构掘进姿态的作用。

型材截面优化选型算法的研究与应用

设计了一种型材截面优化选型算法,以实现型材刚度和质量的最优化配置,解决型材截面优化选型问题。将该算法应用于某天线舱框架的结构设计,以天线舱框架刚度为约束条件,实现了天线舱框架质量最小化的设计指标,同时达到了缩短项目研制周期的效果。

输电线路带电跨越封网施工用绳索的选型算法

[目的]为解决承载索在输电线路带电跨越封网施工过程中因选型不合适而发生断线的问题,基于架空线机械性能力学计算方法,提出了一种承载索的选型算法,使施工安全且高效。[方法]首先,对绳索材料的抗拉强度、伸缩率和单位长度重量进行对比,确定了承载索的材料,即迪尼玛绳;然后以架空线空间力学平衡方程为基础,推导了承载索的力学计算模型;最后,通过力学计算模型提出了承载索的选型算法。[结果]以阿克苏库车750 kV变电站送出工程为实例,运用承载索选型算法选出的承载索满足施工要求。[结论]验证结果表明提出的承载索的选型算法可行,可为实际应用提供指导。

整体刚构体系桥梁结构选型算法研究

为解决整体刚构体系桥梁在结构选型方面面临的方案数量庞大、计算过程繁杂问题,以整体刚构体系桥梁的基本力学模型为基础,开展整体刚构体系桥梁结构选型算法研究,构建了基于杆系有限元理论的计算方法,并通过计算机编程语言Python完成结构选型软件的开发,且内力计算结果与Midas计算结果相比的平均误差在3%以下。

泵的CAD Auto选型优化算法

提出了泵的CADAuto选型优化算法．论述了泵和管路的性能曲线方程及泵安全量选定．建立了泵的选型优化约束条件及工作点的数值计算方法．针对大连水泵厂生产的泵系列进行了算例编程运算结果表明，该算法使泵选用合理．最佳地满足用户运行条件及高效率要求．

变频钻机能耗制动系统研究

结合钻井工艺下钻要求,简单介绍了变频钻机能耗制动系统的主要环节、单元构成,制动单元和电阻的配置原则。以常见的ZJ50/70DB钻机、几个特定钻井参数、特定斩波单元为例,按照一般工况、绞车输入功率、最大钩载3种情况,计算了下钻过程中的回馈能量、功率等制动系统选型参数;提出了变频钻机能耗制动系统配置算法。

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

Smartphone Malware Detection Model Based on Artificial Immune System

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

Adaptive quantile regression with precise risk bounds

An adaptive local smoothing method for nonpaxametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also al- lows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algo- rithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.

A SELF-SIMILAR LOCAL NEURO-FUZZY MODEL FOR SHORT-TERM DEMAND FORECASTING

This paper proposes a selfsimilar local neurofuzzy （SSLNF） model with mutual informati onbased input selection algorithm for the shortterm electricity demand forecasting. The proposed self similar model is composed of a number of local models, each being a local linear neurofuzzy （LLNF） model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree （NLOLIMOT） learning algorithm which partitions the input space into axisorthogonal subdomains and then fits an LLNF model and its associated validity function on each subdomain. Furthermore, the proposed approach allows different input spaces for rule premises （validity functions） and consequents （local models）. This appealing property is employed to assign the candidate input variables （i.e., previous load and temperature） which influence shortterm electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from shortterm load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.

Statistical and Geometrical Way of Model Selection for a Family of Subdivision Schemes

The objective of this article is to introduce a generalized algorithm to produce the m-point n-ary approximating subdivision schemes(for any integer m, n ≥ 2). The proposed algorithm has been derived from uniform B-spline blending functions. In particular, we study statistical and geometrical/traditional methods for the model selection and assessment for selecting a subdivision curve from the proposed family of schemes to model noisy and noisy free data. Moreover, we also discuss the deviation of subdivision curves generated by proposed family of schemes from convex polygonal curve. Furthermore, visual performances of the schemes have been presented to compare numerically the Gibbs oscillations with the existing family of schemes.