Study of algorithms of new slender ship theory of wave resistance

In this paper, Noblesse's New Slender-Ship Wave-Making Theory was investigated numerically. Detailed expressions of zeroth and lst order wave resistance have been derived and calculation programs have also been compiled. In the single and double integral terms of Green function, the kernel function of wave resistance expression, special function expansion method and Chebyshev polynomials approach have been adopted respectively, which greatly simplify the calculation and increase the convergence speed.

QUANTUM COLLISION SEARCH ALGORITHM AGAINST NEW FORK-256

In order to improve the attack efficiency of the New FORK-256 function, an algorithm based on Grover's quantum search algorithm and birthday attack is proposed. In this algorithm, finding a collision for arbitrary hash function only needs O(2m/3) expected evaluations, where m is the size of hash space value. It is proved that the algorithm can obviously improve the attack efficiency for only needing O(2 74.7) expected evaluations, and this is more efficient than any known classical algorithm, and the consumed space of the algorithm equals the evaluation.

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

Parameters of new three-water model based on nuclear magnetic experiment and optimization algorithm

Clastic rock reservoir is the main reservoir type in the oil and gas field.Archie formula or various conductive models developed on the basis of Archie’s formula are usually used to interpret this kind of reservoir,and the three-water model is widely used as well.However,there are many parameters in the threewater model,and some of them are difficult to determine.Most of the determination methods are based on the statistics of large amount of experimental data.In this study,the authors determine the value of the parameters of the new three-water model based on the nuclear magnetic data and the genetic optimization algorithm.The relative error between the resistivity calculated based on these parameters and the resistivity measured experimentally at 100%water content is 0.9024.The method studied in this paper can be easily applied without much experimental data.It can provide reference for other regions to determine the parameters of the new three-water model.

Highly Accurate Golden Section Search Algorithms and Fictitious Time Integration Method for Solving Nonlinear Eigenvalue Problems

This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less,where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector.1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues.Simultaneously,the real and complex eigenvectors can be computed very accurately.A simpler approach to the nonlinear eigenvalue problems is proposed,which implements a normalization condition for the uniqueness of the eigenvector into the eigenequation directly.The real eigenvalues can be computed by the fictitious time integration method(FTIM),which saves computational costs compared to the one-dimensional golden section search algorithm(1D GSSA).The simpler method is also combined with the Newton iterationmethod,which is convergent very fast.All the proposed methods are easily programmed to compute the eigenvalue and eigenvector with high accuracy and efficiency.

Apriori算法及其改进算法Apriori-new的应用分析

对Apriori算法进行应用分析,针对其不足之处,提出一种新的改进型算法——Aprior-new,给出该算法的工作过程,并对该算法进行应用分析。将两种算法进行应用对比,结果表明,Apriori-new算法占用内存少,执行效率更高。

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

A CLASS OF COLLINEAR SCALING ALGORITHMS FOR UNCONSTRAINED OPTIMIZATON

A Class of Collinear Scaling Algorithms for Unconstrained Optimization. An appealing approach to the solution of nonlinear optimization problems based on conic models of the objective function has been in troduced by Davidon (1980). It leads to a broad class of algorithms which can be considered to generalize the existing quasi-Newton methods. One particular member of this class has been deeply discussed by Sorensen (1980), who has proved some interesting theoretical properties. In this paper, we generalize Sorensen’s technique to Spedicato three-parameter family of variable-metric updates. Furthermore, we point out that the collinear scaling three- parameter family is essentially equivalent to the Spedicato three-parameter family. In addition, numerical expriments have been carried out to compare some colliner scaling algorithms with a straightforward implementation of the BFGS quasi-Newton method.

Study of improved gray predictive PID control algorithm

According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.

Density Structure of the Papua New Guinea-Solomon Arc Subduction System

The Papua New Guinea-Solomon(PN-SL)arc is one of the regions with active crustal motions and strong geological actions.Thus,its complex subduction system makes it an ideal laboratory for studying the initiation mechanism of plate subduction.However,the PN-SL subduction system has not yet been sufficiently studied,and its density structure has yet to be revealed.In this paper,we used the free-air gravity data,Parker-Oldenburg density surface inversion method,and the genetic algorithm density inversion method to obtain the density structure of an approximately 1000-km-long northwest-southeast line crossing the PN-SL subduction system under the constraints of the CRUST1.0 global crustal model,onshore seismic data,and the LLNL-G3Dv3 global P-wave velocity model.The density structure shows that density differences between the plates on the two sides of the trench could play a significant role in plate subduction.

Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme

Summary:Throughout the duration of the New Cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS fund.Our study was conducted to examine the prevalence of out-of^county hospitalizations and its related factors,and to provide a scientific basis for follow?up health insurance policies.A total of 215 counties in central and western China from 2008 to 2016 were selected.The total out-of-county hospitalization rate in nine years was 16.95%,which increased from 12.37%in 2008 to 19.21%in 2016 with an average annual growth rate of 5.66%.Its related expenses and compensations were shown to increase each year,with those in the central region being higher than those in the western region.Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region(XI),rural population(X2),per capita per year net income(X3),per capita gross domestic product(GDP)(X4),per capita funding amount of NCMS(X5),compensation ratio of out-of^county hospitalization cost(X6),per time average in-county(X7)and out-of-county hospitalization cost(X8).According to Bayesian network(BN),the marginal probability of high out-of^county hospitalization rate was as high as 81.7%.Out-of^county hospitalizations were directly related to X8,X3,X4 and X6.The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors,economy factors,regional characteristics and NCMS policy factors was 95.7%,91.1%,93.0% and 88.8%,respectively.And how these factors affect out-of-county hospitalization and their interrelationships were found out.Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations,and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.

On Over-Relaxed Proximal Point Algorithms for Generalized Nonlinear Operator Equation with (A,η,m)-Monotonicity Framework

In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.

数据、算力和算法结合反映新质生产力的数字化发展水准

数字经济的发展促进新的劳动主体、新的生产工具和新的生产要素不断涌现,为人们认识世界和改造世界创造了新模式,新质生产力的数字化发展是新质生产力在数字经济领域的表现。数字经济时代,数据作为新质生产力数字化发展的新要素,算力体现新质生产力数字化发展的新动能,算法反映新质生产力数字化发展的新优势,数据、算力和算法的结合反映了新质生产力的数字化发展水准,形成数字时代的新质生产力。新发展阶段,数据、算力和算法的结合首先引起生产力的决策革命,其次引起生产力的工具革命、劳动力革命、生产要素革命和技术—经济范式革命,进一步推动新质生产力的数字化发展。在全球经济新周期的背景下,提高新质生产力的数字化发展水平已经成为推动经济社会高质量发展的关键力量。

采用融合遗传算法的高速公路服务区综合能源系统优化调度研究

为达成“碳中和”目标愿景、促进公路交通系统与新能源的融合,以高速公路服务区为研究对象,考虑服务区内电、冷、热、气共4种负荷需求,构建了包含风光发电的新能源发电方式和电转气设备的高速公路服务区综合能源系统。在此基础上,以风电、光伏出力日前预测和多能负荷日前消耗为输入,各能源设备出力及购能分配为输出,以总成本最低为目标函数,考虑能量平衡、设备安全、运行状态等约束,建立了高速公路服务区综合能源系统优化调度模型。针对高速公路服务区综合能源系统调度问题,设计了遗传-序列二次规划融合优化算法,并以某服务区夏季典型日为例进行验证。结果表明:所构建的调度系统能够有效消纳可再生能源出力,协调外部购电、购气的比例,最终达到降低成本的效果;所提融合算法的调度结果与传统遗传算法、传统序列二次规划算法相比,在成本上分别降低了11.52%、0.70%,求解耗时仅为传统遗传算法的6.7%,独立性相比传统序列二次规划算法得到了提高。

基于改进KNN算法的新能源发电单元运行状态识别

目前识别发电单元运行状态的研究较少,数据来源以数据采集与监控系统为主,采集速度较慢。为此,提出了一种基于发电单元机端电气量数据并融合改进k近邻(KNN)算法的新能源发电单元状态识别方法,直接采集机端电气量数据用于快速判断发电单元状态。提出KNN算法的改进策略,克服了传统KNN算法准确度低、识别速度慢的缺点。利用电力系统分析综合程序获取用于状态识别的发电单元机端电气量数据,利用改进策略对数据进行预处理,并对比传统KNN算法、逐条使用改进策略的KNN算法对新能源发电单元状态识别的耗时与准确度。结果表明所提算法较传统算法的识别准确度和速度明显提升,能满足稳定控制过程中对新能源发电单元的状态感知需求。

含新能源和电动汽车充电站并网的主动配电网无功优化模型

针对新能源和电动汽车充电站规模化并网引起的无功优化问题,首先建立以主动配电网运行成本和电压偏差最小,系统稳定性最优的多目标无功优化模型。其次,基于PV曲线上、下半支解的接近程度,提出一种新的静态电压稳定性指标,以改善系统电压稳定性。最后,采用具有交叉反馈机制的混合优化算法对模型进行求解,并提出一种基于Tent混沌映射和自适应变异算子的改进飞蛾扑火算法提高求解效率。仿真结果表明,所提模型在降低系统运行成本,减少电压波动的同时,还能提高系统稳定性,且该混合优化算法能够快速、准确地求解混合整数的非凸、非线性优化模型。

以高水平法治促进新质生产力发展

新质生产力以全要素生产率大幅提升为核心标志,特点是创新,关键在质优,本质是先进生产力。数据与算法、算力作为质优的创新型要素,优化其配置成为提高全要素生产率的关键一环。在优化要素配置过程中,数据流通、算法开发与算力协同都离不开法治的支撑,同时,整体营商环境的持续优化也需要法治的保障。从实践出发,良法善治在消除和打通新质生产力发展的堵点痛点方面能发挥积极作用,以促进新型生产关系适配新质生产力发展。为此,需在法治框架下健全数据基础制度,推动数据要素有序开放流通,完善人工智能算法治理体系和算力协同机制,持续优化营商环境,为新质生产力发展提供质优生态。