关于构建民营企业竞争战略评价体系的实证研究——以Power Process理论为基础

本文以韩国仁荷大学教授李庆焕提出的Power Process为理论基础,针对民营中小企业的竞争战略进行实证研究,确定了民营企业竞争战略评价体系测评中的48个影响因素,对其进行排序并赋予权值,为企业选用评价指标提供了实用依据.

Power-Gas-Power可再生能源氢电耦合系统核心工艺设计

绿色、低碳是人类能源利用方式转变的发展趋势,但可再生能源的随机性、间歇性和波动性与电网在时间-空间维度的不协调限制了其充分利用。氢能是高效、绿色可持续的二次能源,电解水制氢技术是目前绿氢的主要来源。Power-Gas-Power氢电耦合系统是以氢储能为媒介,调节能量的储存和转化,是缓解弃风、弃光等问题的有效手段。系统总结了Power-Gas-Power可再生能源氢电耦合系统四个核心工艺的设计过程,包括可再生能源发电系统、绿氢制取系统、绿氢储存系统和燃料电池发电系统,最后指出了可再生能源氢电耦合应用过程面临的问题和相关对策。

Bayesian estimation of a power law process with incomplete data

Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable systems.Although statistical inference on the parameters of the power law process has been well developed,numerous studies largely depend on complete failure data.A few methods on incomplete data are reported to process such data,but they are limited to their specific cases,especially to that where missing data occur at the early stage of the failures.No framework to handle generic scenarios is available.To overcome this problem,from the point of view of order statistics,the statistical inference of the power law process with incomplete data is established in this paper.The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method.Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified framework.The results show that the proposed method has more flexibility and more applicability.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

Nanocrystallization of Cementite in 0.4C-1Cr Steel During High-Power Surface Processing

The microstructures of the nanocrystalline surface layer of a quenched and high temperature tempered 0. 4C- 1Cr steel induced by high-power surface processing （HPSP） technique were characterized by scan- ning eleetron microscopy and transmission electron microscopy. The results indicate that a nanocrystalline layer was fabricated on the surface of the steel 19 using HPSP treatment. The mean grain size in the surface layer is about 11 nm. The nanocrystallization of cementite is prior to that of the matrix phase, ferrite.

The Effectiveness of the Squared Error and Higgins-Tsokos Loss Functions on the Bayesian Reliability Analysis of Software Failure Times under the Power Law Process

Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.

Branching Process based Cascading Failure Probability Analysis for a Regional Power Grid in China with Utility Outage Data

Studying the propagation of cascading failures through the transmission network is key to asses and mitigate the risk faced the energy system. As complex systems the power grid failure is often studied using some probability distributions. We apply 4 well-known probabilistic models, Poisson model, Power Law model, Generalized Poisson Branching process model and Borel-Tanner Branching process model, to a 14-year utility historical outage data from a regional power grid in China, computing probabilities of cascading line outages. For this data, the empirical distribution of the total number of line outages is well approximated by the initial line outages propagating according to a Borel-Tanner branching process. Also for this data, Power law model overestimates, while Generalized Possion branching process and Possion model underestimate, the probability of larger outages. Especially, the probability distribution generated by the Poisson model deviates heavily from the observed data, underestimating the probability of large events (total no. of outages over 5) by roughly a factor of 10-2 to 10-5. The observation is confirmed by a statistical test of model fitness. The results of this work indicate that further testing of Borel-Tanner branching process models of cascading failure is appropriate, and should be further discussed as it outperforms other more traditional models.

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.

Comparison of Risk Assessment for a Nuclear Power Plant Construction Project Based on Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process

Recently, plant construction throughout the world, including nuclear power plant construction, has grown significantly. The scale of Korea’s nuclear power plant construction in particular, has increased gradually since it won a contract for a nuclear power plant construction project in the United Arab Emirates in 2009. However, time and monetary resources have been lost in some nuclear power plant construction sites due to lack of risk management ability. The need to prevent losses at nuclear power plant construction sites has become more urgent because it demands professional skills and large-scale resources. Therefore, in this study, the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) were applied in order to make comparisons between decision-making methods, to assess the potential risks at nuclear power plant construction sites. To suggest the appropriate choice between two decision-making methods, a survey was carried out. From the results, the importance and the priority of 24 risk factors, classified by process, cost, safety, and quality, were analyzed. The FAHP was identified as a suitable method for risk assessment of nuclear power plant construction, compared with risk assessment using the AHP. These risk factors will be able to serve as baseline data for risk management in nuclear power plant construction projects.

A Parallel Processing Uninterruptible Power Supply for Sudden Voltage Fluctuation for Power Management

This paper deals with a parallel processing uninterruptible power supply (UPS) for sudden voltage fluctuation in power management to integrate power quality improvement, load voltage stabilization and UPS. To reduce the complexity, cost and number of power conversions, which results in higher efficiency, only one voltage-controlled voltage source inverter (VCVSI) is used. The VCVSI is connected in series on the DC battery side and in parallel on the AC grid side with a decoupling inductor. The system provides sinusoidal voltage at the fundamental value of 220V/60Hz for the load during abnormal utility power conditions or grid failure. Also, the system can be operated to mitigate the harmonic current and voltage demand from nonlinear loads and provide voltage stabilization for loads when sudden voltage fluctuation occur, such as sag and swell. The experimental results confirm the system protects against outages caused by abnormal utility power conditions and sudden voltage fluctuations and change.

空气预热器的设计计算和数值分析

对完成设计的工艺目标,将室温空气加热到一定温度时,空气预热器需要的电加热功率进行计算,并对相关设备结构进行设计。利用Workbench有限元分析软件建立空气预热器流场的数值分析模型,分析设备内流场情况,从理论上了解具体的混合和传热状态,考察工艺计算和设计的正确性。此外,还研究了相关工艺参数和结构设计对设备性能的影响,认为提高空气处理量,会快速降低出口温度,并显著提高设备阻力,但空气受热更加均匀;增加加热功率,出口温度和设备阻力逐渐升高,但空气受热均匀程度有所降低。此外,设备内布置折流板,能够有效促进流场湍流程度,提升设备出口温度,增加混合传热效果,但会带来较大的设备阻力。

Low-Power Operational Amplifier for Real-Time Signal Processing System of Micro Air Vehicle

A low-power complementary metal oxide semiconductor（CMOS） operational amplifier （op-amp） for real-time signal processing of micro air vehicle （MAV） is designed in this paper.Traditional folded cascode architecture with positive channel metal oxide semiconductor（PMOS） differential input transistors and sub-threshold technology are applied under the low supply voltage.Simulation results show that this amplifier has significantly low power,while maintaining almost the same gain,bandwidth and other key performances.The power required is only 0.12 mW,which is applicable to low-power and low-voltage real-time signal acquisition and processing system.

Combining the People Power and the Process Power to Achieve Six Sigma Process Capability——A Case Study

Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six-Sigma implementation case study carried out in a magnet manufacturing compa ny, which produces bearing magnets to be used in energy meters. If the thickness of the produced bearing magnets is between 2.35 mm and 2.50 mm, they will be ac cepted by the customers. All the time the company could not produce the bearing magnets within the specified thickness range, as their process distribution was flat with 2.20 mm as lower control limit and 2.60 mm as upper control limit. This resulted in a huge loss in the form of non-conformities, loss of time and goodwill. The process capability of the company then was around 0.40. Organisat ion restructuring was carried out to reap the benefit of the People Power of the organisation. Statistically designed experiments (Taguchi Method based Design o f Experiments), Online quality control tools (Statistical Process Control To ols) were effectively used to complete the DMAIC (Define, Measure, Analyse, Impr ove and Control) cycle to reap the benefit of the Process Power of the organisat ion. Presently the company enjoys a process capability of 1.75, a way towards Si x-Sigma Process Capability.

新型电力系统数据跨域流通泛安全边界防护技术

新型电力系统建设涉及多业务系统、多部门、多方主体间进行海量、异构数据的交互和共享,电力数据的内外部网络环境与安全形势日趋复杂化,数据流通的脆弱性风险加剧。首先,分析新型电力系统下数据流的类型与特性,概括电力数据流通安全防护面临的新形势;其次,基于专用数据处理器(DPU)的高性能流量编排和多功能安全网关能力,构建面向电力数据跨域流通安全增强的泛安全边界,凭借数据面可编程技术沟通网络安全与数据安全双维度安全能力,提出基于DPU的数据跨域流通协同防护技术应用方案;最后,阐释DPU在不同电力通信网络层次的部署方式、价值与关键技术,分析现阶段DPU在电力领域应用存在的挑战。

基于遗传算法的双压氢液化流程性能优化研究

由于液氢具有储氢密度高等显著优势,因此氢液化技术是实现氢大规模化应用的关键技术之一。构建了双压Claude循环的氢膨胀制冷氢液化循环流程,调用Refprop软件物性库确保物性数据的准确性,利用遗传算法以总比功耗为优化目标,开展了氢液化流程热力参数设计与优化。优化后流程氢液化率达到6.5 t/d,液氢产品仲氢含量95%,总比功耗为10.53 kW·h/kg,流程(火用)效率为35.72%。给出了低温各部件(除却压缩机和水冷器部分)的(火用)损失分布和各部件(火用)损失占比;可为大型工业级氢液化装置研制提供参考。

一种双天线GNSS接收机混合干扰抑制方法

相对于单天线GNSS接收机,阵列GNSS接收机具有空间分辨能力,当干扰信号与卫星信号在时域频域上产生混叠时,其能够从空域上对干扰信号进行抑制,具有更强的干扰抑制能力。但阵列GNSS接收机相对于单天线GNSS接收机需要更多的阵元,随着阵元数目的增加,系统成本也相应的增加,限制了阵列GNSS接收机的应用范围。双天线GNSS接收机既具有空域抗干扰能力,同时又具有价格低廉的特点,是一种较好的折中选择。对于单一的连续波干扰,其能够产生很好的抑制效果,但是当连续波干扰中混有脉冲干扰时,由于受到自由度的限制,双天线GNSS接收机无法对混合干扰进行有效抑制,进而影响接收机的正常工作。针对于上述问题,本文提出一种新的混合干扰抑制算法。首先利用脉冲的时域特征,对待处理信号进行分块处理,确保至少有一个数据块中不含有脉冲干扰,随后对不含脉冲干扰的数据块,使用空时最小功率(Space-Time Adaptive Processing Power Inversion,STAP-PI)算法得到最优权值,然后利用该权值抑制原信号中的连续波干扰。最后,对处理之后信号中残余的脉冲干扰进行时域置零处理,从而达到抑制混合干扰的目的。仿真实验和实采实验结果均证明了所提算法的有效性。

老油田掺水工艺能耗分析及节能优化技术研究

为实现老油田地面掺水工艺的简化优化,在水力和热力计算的基础上,利用Pipesim软件实现了双管掺水工艺、首端环状掺水工艺和末端环状掺水工艺的建模,研究了不同掺水方式、掺水量及掺水温度对能耗和运行费用的影响,并以总运行费用最低为目标函数,对不同工况下的最佳运行参数进行了求解。研究结果表明:随着掺水温度的升高,掺水系统的耗电量有所下降,随着掺水比的增加,耗电量呈先增加后降低再增加的趋势;耗气量与掺水温度和掺水比均呈正相关,在掺水比1.4之后的耗气量增幅不大;双管掺水工艺的运行费用最高,末端环状掺水工艺的运行费用最低。通过最优化求解,获得了不同月份的最优掺水温度和掺水量,工艺简化后,运行费用降低了63.3%~68.8%,年运行费用降低了37.31万元,节能潜力巨大。研究结果可为同类老油田地面方案的实施提供实际参考。

电力变压器关键尺寸视觉检测方法及其缩比模型验证

电力变压器的关键尺寸测量是其装配制造、运输安装过程中重要一环,现有测量方法操作繁琐且效率较低。为此,本文提出了一种适用于110 kV油浸式电力变压器关键尺寸视觉检测方法,该方法利用YOLO目标检测算法、Grabcut图像分割算法实现关键组部件的智能检测与分割,然后基于双目立体视觉原理实现套管相间距离及变压器最大截面外形尺寸等关键尺寸的测量。本文搭建了110 kV油浸式电力变压器外观缩比模型,试验分析了拍摄距离和角度等因素对电力变压器关键尺寸视觉检测的影响。结果表明,本文基于缩比模型试验实现了在不同拍摄距离与角度下变压器关键尺寸的视觉检测,验证了该方法的有效性。本文方法可以为110 kV油浸式电力变压器现场尺寸测量提供参考。

模态分解的电能质量检测探究实验教学实践

针对“现代信号处理”课程的教学难点,以模态分解法(EMD)的电能质量扰动信号检测为例,进行了探究性实验教学仿真与实践。在教学过程中,引导学生对传统EMD电能质量检测法精度偏低的问题进行探究。然后根据探究结果,融合SVD滤波、ICEEMDAN及排列熵(PE)等多种现代信号处理技术,提出了SVD-ICEEMDAN-PE的电能质量扰动检测方法,能够提高精测精度。在此基础上,设计信号降噪、算法性能分析、扰动信号检测、实验信号检测等多个仿真对方法的有效性进行了验证。探究性实验教学是理论教学的延伸和拓展,教学过程注重以学生为中心,引导学生体会思考-探索-解决问题的科研方法,从而激发学生求学—求知—钻研的科研精神。

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。