Using multiple metrics for analysis of improvement

To make global decisions about a project or group of projects, it is necessary to analyse several metrics in concert, as well as changes in individual metrics. This paper discusses several approaches to collective metrics analysis. First, classification tree analysis is described as a technique for evaluating both process and metrics characteristics. Next, the notion of a multiple metrics graph is introduced. Developed initially as a way to evaluate software switch quality, a multiple metrics graph allows collections of metrics to be viewed in terms of overall product or process improvement.This work was done while the authors were affiliated with Contel Technology Center, Chantilly, Virginia, USA.     

参数辨识在陀螺仪性能分析中的应用

基于挠性陀螺仪动力学模型, 研究参数辨识在陀螺仪性能分析中的应用方法。在论述挠性陀螺仪性能指标和力学模型的基础上, 利用伪随机多谐波信号作为激励信号, 通过零阶保持器将离散模型转换为连续模型进行参数辨识。实验结果显示, 模型预测输出与实际测量数据的拟合度达到95. 3%, 残差在合理的公差带以内。因此, 参数辨识是陀螺仪性能分析中的一个有效性方法, 可为陀螺仪故障分析和控制回路设计提供一种技术手段和理论依据。     

基于动态插桩的程序分析工具的性能改进

基于动态插桩的工具被广泛应用于程序分析中, 但该类工具都面临着严重的性能问题。这类工具的性能开销主要由两部分组成, 即插桩引擎的开销和用户定义的分析程序的开销。为降低用户定义的分析工具的开销, 首先分析了基于动态插桩的工具的性能开销的组成, 并通过实验分析了造成性能开销的几点原因及其对工具的性能影响; 根据分析结论提出了使用离线分析方式来优化工具性能, 最后通过并行数据收集来进一步提升工具性能。使用该方法能减少分析程序5%～15%的CPU占用时间。     

水电厂励磁系统故障分析及改进措施研究

励磁系统直接关系着水电厂电力生产工作进度和任务完成质量,为此,提出水电厂励磁系统故障分析及改进措施研究;在故障分析阶段,通过压力传感器进行磁系统故障录波,并放大、滤波及隔离处理,以此作为样本,输入到人工神经网络当中,实现励磁系统故障识别分析;在故障改进阶段,针对故障分析结果,对常见的几种故障,即功率柜故障、调节器故障、失磁故障、整流电源故障、熔断器爆裂故障、逆变灭磁失败故障等,进行改进研究,提出改进措施;实验结果表明:利用基于神经网络算法的方法分析水电厂励磁系统故障,证明分析的准确性,为后续改进措施的提出提供了可靠依据。     

An analysis of flexibility and quality improvement in a quality-adjusted EOQ model with finite-range stochastic lead-time

Traditional Economic Order Quantity (EOQ) model operates under a series of highly restrictive assumptions, including, but not limited to, the assumptions of deterministic lead-time, perfect quality, and constant setup cost. Many variants of the traditional EOQ model have been developed as a result of relaxing one or more of these assumptions. These variants include a quality-adjusted EOQ model with finite-range stochastic lead-time which relaxed the deterministic lead-time and perfect quality assumptions. Utilizing the basic framework of the quality-adjusted model, the authors also considered the option of investment to improve the quality and obtained closed form relationships for the quality-adjusted and quality improvement models. This paper extends the previous work by investigating two additional models; one aimed at improving flexibility, through setup reduction, and the second considering the strategy of simultaneous investment in flexibility and quality improvement. Analytical and numerical results are presented for both models. These results indicate that significant savings over the quality-adjusted EOQ model with finite-range stochastic lead-time are realized for both the flexibility improvement and simultaneous models.     

Continuous improvement through knowledge-guided analysis in experience feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.     

Contract efficiency for a decentralized supply chain in the presence of quality improvement

In this paper, we study a joint pricing and product quality decision problem in a decentralized supply chain consisting of one manufacturer and one retailer. Although the manufacturer decides the product quality with an associated cost, the retailer decides the retail price. We aim to study and compare different contract formats for this decentralized supply chain. There is a trade‐off in the choice of contracts: simpler format contract (with a few parameters) is less complicated, but the contract efficiency is low. We start with the simplest one‐parameter contract: a wholesale price contract that serves as the benchmark. We then study how contract efficiency can be improved by adding one more parameter. Specifically, we consider three two‐parameter contracts that are commonly used in reality: two‐part tariff contract, revenue‐sharing contract, and effort cost sharing contract. We find that the contract efficiency is improved under all the three contracts, but in different ways: the improvement in contract efficiency under each of them dominates the other two when manufacturer's quality improvement effectiveness is relatively low, moderate, and high, respectively. Furthermore, through numerical examples, we find that under some cases, a choice from these three two‐parameter contracts can achieve a close‐to‐perfect efficiency (>85%). Finally, we investigate whether a combination of the three two‐parameter contracts can achieve coordination. Interestingly, we find that only the combination of effort cost sharing contract and revenue‐sharing contract can achieve coordination, whereas combinations of either of them and two‐part tariff contract cannot.     

An analysis of contract form for supply chains with quality improvement

This study examines different contract forms for the supply chain with quality improvement decision and retailer price decision. We consider three types of quality improvement: cost consuming, cost identical, and cost saving, which correspond to the cases in which quality improvement leads to an increment, no change, and a decrease in production cost, respectively. We compare the performance of two types of quality contracts: (1) pay‐before‐performance contract, under which the supplier receives monetary support from the buyer before exerting quality improvement effort; and (2) pay‐after‐performance contract, under which the supplier receives monetary compensation based on the outcome of effort. We find that the performance of each contract depends on the types of quality improvement. Further, the pay‐after‐performance contract leads to close‐to‐perfect contract efficiency and dominates the pay‐before‐performance contract from the supplier's perspective in most cases. However, this result does not apply in two extended cases: when there are multiple competing suppliers, the pay‐before‐performance contract can dominate the pay‐after‐performance contract if quality improvement is cost consuming; and when the buyer can exert sales effort, neither contract can achieve close‐to‐perfect efficiency in most cases.     

基于PSO算法的某型飞机起飞阶段参数辨识及性能分析

飞机性能分析是飞行训练评估的重要组成部分具有重要的应用价值。结合某型军用战机起飞阶段飞参数据,研究了利用粒子群算法(PSO)进行参数辨识并进行飞机性能分析的问题。首先通过动力学建立了起飞性能数学模型;然后将动力学方程转化为以速度的平方为输入量,速度增量为输出量的状态方程,利用PSO进行识别并得到了待辨识的参数,并具有较高的精度;最后将辨识的参数代入动力学方程针对影响起飞性能的起飞质量和温度进行了分析,得到在极端条件下飞机起飞性能。可以为日后选择最佳性能飞机作战出动提供决策参考。     

A theoretical analysis of recursive identification methods

In this paper five different recursive identification methods will be analyzed and compared, namely recursive versions of the least squares method, the instrumental variable method, the generalized least squares method, the extended least squares method and the maximum likelihood method. They are shown to be similar in structure and need of computer storage and time. Making use of recently developed theory for asymptotic analysis of recursive stochastic algorithms, these methods are examined from a theoretical viewpoint. Possible convergence points and their global and local convergence properties are studied. The theoretical analysis is illustrated and supplemented by simulations.     

非对称成对载波多址信号的相位误差分析及幅度改进算法

针对由主站和小站信号同频混合而成的非对称成对载波多址（PCMA）信号解调问题，构建了一种实现此类信号解调的框架。参数估计是非对称PCMA通信系统在实现两路信号分离解调时不可或缺的环节，对于幅度参数估计精度问题，提出一种基于四次方法的搜索式幅度估计算法。首先建立非对称PCMA系统解调模型并作出基本假设，然后对不同假设下的相位误差进行对比并分析相位误差对幅度估计算法的影响，最后提出一种新的幅度估计算法。实验结果表明在相同信噪比（SNR）下，正态相位误差下的小站信号解调性能要劣于其均值条件下的解调性能。当误比特率（BER）在数量级为10^-4时，改进算法下小站信号的解调性能提升了1 dB，说明改进算法优于四次方法。     

过程改进中评审活动的模拟分析

为定量研究评审效用,改进软件过程,通过模拟定量计算评审在过程改进中的效用。基于系统动力学方法分析评审活动和软件过程,建立软件过程模型。结合实例,证明了模型的正确性,通过模拟定量分析评审对项目工作量和工期的作用。模拟结果证明在一定范围内增加评审投入可以有效地增加工作效率,提高进度。以成本为优化目标,设置生命周期各阶段评审比例,通过自动模拟,得到最优评审工作投入,为实施软件过程改进提供量化支持。     

提高模态参数识别精度的分量分析新算法

为了利用某一系统的输入和输出数据,快速、准确地提取该系统的模态参数,该文介绍了模态参数识别中最基本的一种方法,即分量分析法^[2],提出了一种提高其模态参数识别精度的新算法。并且,举出一个实例证实了此方法的正确、可靠性。该算法最大优点是原理简单、易懂,方法容易实现,而且,可以不依赖人工操作,利用程序可以直接把需要的参数高精度地识别出来。     

基于Abaqus的GE17自润滑关节轴承结构分析与改进

针对在GE17自润滑关节轴承轴向静载试验中,轴承内圈轴向位移大于许用值的问题,应用Abaqus建立GE17轴向静力加载模型进行分析,结果表明轴承外圈端面的安装沟槽削弱轴承的轴向刚度。改进GE17轴承结构,减小轴承球径尺寸,增加沟槽内挡边厚度,提高其抗挤压变形能力。试验结果表明,改进后结构在额定载荷下的轴向位移减小10.7%,从而验证仿真结果的准确性和结构改进的有效性,为同类型轴承设计提供参考。     

Geometric and elastostatic calibration of robotic manipulator using partial pose measurements

The paper deals with the geometric and elastostatic calibration of robotic manipulator using partial pose measurements, which do not provide the end-effector orientation. The main attention is paid to the efficiency improvement of identification procedure. In contrast to previous works, the developed calibration technique is based on the direct measurements only. To improve the identification accuracy, it is proposed to use several reference points for each manipulator configuration. This allows avoiding the problem of non-homogeneity of the least-square objective, which arises in the classical identification technique with the full pose information (position and orientation). Its efficiency is confirmed by the comparison analysis, which deals with the accuracy evaluation of different identification strategies. The obtained theoretical results have been successfully applied to the geometric and elastostatic calibration of a serial industrial robot employed in a machining work cell for aerospace industry.     

Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomalies

This paper describes a new methodology to detect small anomalies in high resolution hyperspectral imagery, which involves successively: (1) a multivariate statistical analysis (principal component analysis, PCA) of all spectral bands; (2) a geostatistical filtering of noise and regional background in the first principal components using factorial kriging; and finally (3) the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values and anomalies. The approach is illustrated using 1 m resolution data collected in and near northeastern Yellowstone National Park. Ground validation data for tarps and for disturbed soils on mine tailings demonstrate the ability of the filtering procedure to reduce the proportion of false alarms (i.e., pixels wrongly classified as target), and its robustness under low signal to noise ratios. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e., RX detector which computes the Mahalanobis distance between the vector of spectral values and the vector of global means), and fewer false alarms are obtained when using a novel statistic S₂ (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, increasing false alarms, in particular for the identification of the least pure pixels. Results from a mine tailings site demonstrate the approach performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.     

Structural improvement for solder joint failure in ultrasonic plastic assembly

This paper presents a method of structural dynamic analysis with finite element method to identify the cause failure of solder joint caused by the vibration of ultrasonic welding. In this method, explicit FE method and its contact algorithm are used to simulate the energy transmission from vibrational energy to internal energy under some assumptions. Therefore, firstly, its feasibility is verified by 2D coupled thermal–mechanical analysis. Then, based on a certain type of cell phone battery, a 3D FE model is established for analyzing the vibration of the solder joints during ultrasonic welding. According to the simulation, we analyze the cause of the failure from three aspects: the location of the chip, the shape of the PCB and the structure of the housing. Correspondingly, the improvements are presented and applied to trial manufacture. The result from mass production shows that the improvements can decrease the reject ratio by 90% compared with the original design.     

时间序列预测模型在压缩机零件测试中的运用

压缩机零件在使用时会随时间的变化发生变形,对于压缩机的性能会有很大的影响,因此在零件进行材料选择和几何尺寸设计时,对相关零件变形的检测和预报是至关重要的。介绍了时间序列分析的基本理论及其预测建模的方法;采用参数识别方法,结合压缩机消音板发生变形的动态数据,建立该压缩机零件变形的时序预测模型,对相关零件变形进行了监测和预报,具有很强的实际运用价值。     

Modular analysis and modelling of risk scenarios with dependencies

The risk analysis of critical infrastructures such as the electric power supply or telecommunications is complicated by the fact that such infrastructures are mutually dependent. We propose a modular approach to the modelling and analysis of risk scenarios with dependencies. Our approach may be used to deduce the risk level of an overall system from previous risk analyses of its constituent systems. A custom made assumption-guarantee style is put forward as a means to describe risk scenarios with external dependencies. We also define a set of deduction rules facilitating various kinds of reasoning, including the analysis of mutual dependencies between risk scenarios expressed in the assumption-guarantee style.     

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to （-1, 1）. Based on the deduced principle of acceleration factors, a new adaptive PSO algorithm- harmonious PSO （HPSO） is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.