电力工程计价费用标准测算优化方法研究——基于GRA-MC模型的实证分析

工程计价费用标准是建设项目造价确定的重要依据,对于精准投资和价格科学形成具有重要影响。随着电力工程建设新型技术的发展应用,现行工程计价费用标准的测算与管理面临着新的发展要求与挑战,优化电力工程计价费用标准测算流程具有现实意义。本文将灰色关联分析法、蒙特卡罗模拟法等数理统计理论与方法应用于费用标准测算工作的因素分析、水平测算、检验验证等关键模块,形成一套科学高效的基于GRA-MC的电力工程计价费用标准测算优化方法体系。并以A类型变电站工程中垂直运输费用标准测算为例进行实证分析,研究结果显示:测定的垂直运输费用标准具有一定的代表性及覆盖性,能够作为指导实际工程费用标准测算及水平分析的参考依据,可为费用标准高效更新调整与工程投资精益化管控提供有益参考。

上海市某样本地区重点公共场所巡查人力资源优化研究

目的测算1年中完成重点公共场所卫生监督巡查全覆盖任务所需要的最少人数。方法选取上海市某区作为样本地区,运用地理信息系统(GIS)构建重点公共场所路网分布图,在此基础上运用VRP控件测算完成工作任务所需要的最少时间,进而推算在1年内完成该工作任务所需的最优人员数。结果样本地区共有重点公共场所1 093家,从事公共场所卫生监督工作的共14人,2010年至2012年年均完成监督2 347户次,完成1 093户次需6.5人,优化测算表明,完成1 093户次监督,巡查人员可以减少至4.7人。结论合理设置巡查路线、统筹考虑巡查计划安排,可有效减少人力需求,提高工作效率。本文所采用的方法也可用于测算完成公共场所量化分级监督户次要求所需的最少人员数。

炼油厂日优化管理体系的建立与实施

金陵分公司以管理学中"反馈原理"为理论基础,以R-SIM炼油厂全流程优化软件为技术支持,在全公司范围内建立"日优化"管理体系,加强优化方案测算力度,量化日生产安排,确保每个生产环节时刻处于最优化运行状态。该管理体系应用以来,取得了较好的社会、生态和经济效益。

HYSYS在常减压装置中的应用

文章利用HYSYS软件建立较准确的常减压装置全流量模型,利用常压塔模型优化,将重整料干点从155℃提高至166℃,核算每天可增效3.55万元;减压塔模型通过比较重柴油与轻蜡油两者内混与外混方式外送的经济效益,核算出外混节能效果更好,从而定量指导了实际生产。

Optimizing neural network forecast by immune algorithm

Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.

A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.

Real-valued multi-area self set optimization in immunity-based network intrusion detection system

The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.

Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

炼油专业达标体系及装置竞赛考核方法

炼油专业达标竞赛有利于对企业各项技术经济指标进行全面客观评价分析,明确调整和改进的方向。装置竞赛有利于在不同企业装置间进行对比,促进企业生产运行水平和装置专项指标的提升。然而,随着市场变化和技术进步,部分指标设置可能不再适合当下的实际生产情况。本研究在当前某企业专业达标和装置竞赛体系的基础上,依据效益导向、市场导向和技术导向,对竞赛指标的细项设定、考核规则和分值权重等进行优化和测算,以适应行业市场的变化和装置技术的发展,使其更趋科学化与合理化,为企业炼油达标考核提供评价体系支撑,进一步提升生产管理水平和企业效益。

Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

Optimized Triangulation Algorithm in Terrain Modeling

3D reconstruction of terrain model based on digital line graphics (DLG) is discussed. An auto-coupling triangles algo-rithm based on triangle topological relationship is put forward, and the topological data model of complicated terrain is developed. Based on this data model, automatic 3D topological reconstruction of terrain is realized.

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

Nonlinear least square estimation using difference quotient instead of derivative containing different classes of measurements

Using difference quotient instead of derivative, the paper presents the solution method and procedure of the nonlinear least square estimation containing different classes of measurements. In the meantime, the paper shows several practical cases, which indicate the method is very valid and reliable.

Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Optimal Placement of Phasor Measurement Units Using a Modified Canonical Genetic Algorithm for Observability Analysis

This paper proposes a method for optimal placement of synchronized PMUs （phasor measurement units） in electrical power systems using a MCGA （modified canonical genetic algorithm）, which the goal is to determine the minimum number of PMUs, as well as the optimal location of these units to ensure the complete topological observability of the system. In case of more than one solution, a strategy of analysis of the design matrix rank is applied to determine the solution with the lower number of critical measurements. In the proposed method of placement, modifications are made in the crossover and mutation genetic operators, as well as in the formation of the subpopulation, and are considered restrictive hypotheses in the search space to improve the performance in solving the optimization problem. Simulations are performed using the IEEE 14-bus, IEEE 30-bus and New England 39-bus test systems. The proposed method is applied on the IEEE 118-bus test system considering the presence of observable zones formed by conventional measurements.

Application of Genetic Algorithms to Optimize Neural Networks for Selected Tribological Tests

This paper presents a method of determining the friction coefficient in metal forming using multilayer artificial neural networks based on experimental data obtained from strip drawing test. The number of input variables of the artificial neural network has been optimized using genetic algorithm. This process is based on surface parameters of the sheet and dies, sheet material parameters and clamping force as input parameters to train the neural network. In addition to demonstrating the fact that regression statistics model using genetic selection and intelligent problem solver are better than models without preprocessing of input data, the sensitivity analysis of the input variables has been conducted. This avoids the time-consuming testing of neurons in finding the best network architecture. The obtained results from this study have also pointed out that genetic algorithm can successfully be applied to optimize the training set and the outputs agree with experimental results. This allows reduction or elimination of expensive experimental tests to determine friction coefficient value.

Intelligent Metal Detection and Disposal Automation Equipment Based on Geometric Optimization Driving Algorithm

In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.

中国区域碳中和的技术溢出与能源回弹:机理、实证及启示

构建能源回弹的优化测算模型和影响因素评估模型,对2007—2022年全国整体以及八大综合经济区的能源回弹趋势进行分析。研究表明:(1)在碳中和情境中,区域短、长期能源回弹在考虑技术溢出的条件下均呈现“逆反效应”,而非传统意义上的“完全回弹”或“部分回弹”;(2)碳中和技术创新的节能降耗效果在区域层面会因能源需求增长被部分或全部抵消,且各区域能源回弹对技术溢出的敏感性存在明显差异,局部能源安全与能源转型的压力加剧;(3)相较于正向影响因素,产业结构、低碳化水平、碳排放绩效压力和劳动力基数等负向影响因素才是破除供给侧能源转型与能源安全长期压力的有效“抓手”,而环境治理可在一定程度上释放能源需求侧的转型压力。

Optimization of PGNAA set-up for the elements detection in aqueous solution

A prompt gamma neutron activation analysis(PGNAA)set-up was developed for the elements detection in aqueous solution,which includes a 241Am-Be neutron source and a 4-inch BGO detector.The geometry of set-up is determined by a series of simulations with the MOCA code to improve the efficiency of the elements detection.The thermal neutron flux and the gamma-ray self-absorption are considered during the optimization calculations.Experiments were performed to validate the set-up using samples including chlorine and mercury,respectively.The result shows that the characteristic peak count has linear relationship with the chlorine and mercury concentration changing.The minimum detectable concentrations of chlorine and mercury were found as 54 mg/L and 51.4 mg/L,respectively.