Tools and Methods for the Improvement of Quality

In developing quality-control procedures, a step-loss function has been used implicitly or explicitly to describe consumer perceptions about product quality. A quadratic loss function has been suggested by Taguchi as an alternative to the step-loss function in measuring the loss due to imperfect product quality (cost of acceptance). In this article, Bayesian analyses of the known-standard-deviation acceptance-sampling problem are described for both the step and quadratic loss functions with three cost components—cost of inspection, cost of acceptance, and cost of rejection. A normal prior distribution is used for the lot mean. Efficient procedures for finding minimum expected cost procedures are given. For a particular example, comparisons are made of how optimal sampling plans and costs computed under the two cost structures change as the form of the prior distribution and misspecification of its mean and variance are varied. Sensitivity analyses for both cost functions show that the optimal sampling plan is robust with respect to the form of the prior distribution, as well as to misspecification of its mean and variance, if the tail specification reasonably approximates that of a normal distribution.     

Researches on Laser Isotope Separation of Gadolinium and Boron

Laser isotope separation experiments of Gd and B by atomic and molecular methods, respectively, have been performed. Gadolinium-157 was selectively photoionized by means of three linearly polarized dye lasers, the excitation process of which is based on the polarization selection rules. The effect of magnetic field on isotopic selectivity was discussed. ¹⁰BCl₃ was selectively photodissociated through IR multiphoton dissociation by the irradiation of TEA CO₂ laser or free electron laser (FEL). Selectivity was improved by two-color laser irradiation.     

Conformance and nonconformance in segmentation-free X-ray computed tomography geometric inspection

Additive Manufacturing (AM) is changing the manufacturing paradigm as it makes it possible to generate complex geometries that are impossible using conventional technologies. However, conventional GPS/GD&T practices are inadequate both at specifying and verifying geometric tolerances. In both cases, they lack the required flexibility. Applying volumetric instead of surface representations helps to solve the problem of specifying tolerances and coheres with topological optimization. The verification paradigm must be modified, too, as AM allows an increase in part complexity without a corresponding increase of cost. Among measurement techniques, only X-ray computed tomography (XCT), which is volumetric, is capable of easily measure complex parts. Leaving the discussion of volumetric tolerance specifications to the future, the aim of this work is exploring a part geometric accuracy verification by direct comparison between its nominal geometry and geometric tolerance volumetric representation, and an XCT volumetric image of it. Unlike the conventional use of XCT for geometric verification, this is a segmentation-free verification. The method is based on the “mutual information” of the two, i.e. information shared by the measured and nominal representations. The output is a conformance statement that does rely on a measurement but nor on a specific measured value not rely on a measurement result. This makes defining a decision rule considering consumer's and producer's risks difficult: uncertainty does not exist in this case. Statistic and simulation techniques make it possible to estimate these risks, defining a numerical model of the distribution of the gray values in a specific portion of the XCT image. Finally, an additive manufacturing case study validates the methodology.     

Development of an objective mental workload assessment tool based on Rasmussen's skill–rule–knowledge framework

It is important to monitor operators’ mental workload during the operation phase. Physiological measurement approaches could record the operator's mental data continuously, and might be less interruptive on the work activities. However, these methods often require the attachment of physical sensors, which are not unobtrusive in the physical sense. Furthermore, the individual difference makes calibrating to each individual tedious and requires trained persons to use. Often high noise-to-signal ratio data are hard to analyze. Due to these factors, physiological workload measurements are hardly widely applied in practical fields. In this study, an objective, non-intrusive and performance-based mental workload predictive model was proposed with high validity (R² = 0.51), which can be applied during the operation phrase. This model, developed based on the Rasmussen's skill–rule–knowledge framework, is comprised of two novel cognitive indices, the attention required index and uncertainty index. It can be used as the basis for establishing an early online warning system automatically. Furthermore, this model also predicts the types of error-prone tasks. This kind of information is expected to provide managers and supervisors with opportunities to intervene and improve tasks before error occurred. Finally, the predictive model proposed in this paper requires more practical application in fields to be completed.     

On the linear approximation of mixture internal energies of departure

Direct Monte Carlo simulation of internal energies of departure for binary mixtures of geological interest are gathered and compared to those calculated using a linear mixing rule. Simulation results for gas–oil, oil–oil, and oil–water mixtures show that the linear mixing rule used in the Gibbs–Helmholtz Constrained (GHC) equation of state framework gives accurate approximations of binary mixture internal energies of departure. A flowchart for computing internal energies of departure using Monte Carlo simulation is included along with a sensitivity analysis for the GHC mixture energy parameter with respect to uncertainty in internal energies of departure.     

Rule Construction and Execution for Root Cause Analysis

We developed a root cause analysis (RCA) system for an enterprise IT system environment. In this paper, we propose a rule‐construction method that builds analysis rule data objects from general analysis rules and the topology information of the actual target IT environment. Using this method, the IT administrator can use the RCA system without any manual rule modifications. For the execution of the analysis, we introduce the certainty factor to realize robust analysis when the order and reachability of a fault event cannot be guaranteed.     

使用邮件合并实现多条记录自动分类分页输出

该文介绍了如何利用EXCEL的基本操作和WORD的域和规则来实现邮件合并中多条记录的自动分类分页输出。     

生物冶金ORP与关键工艺参数关联规则的挖掘

针对生物冶金提金率难以提高这一难题,通过深入分析生物冶金工艺,依据工艺现场实测数据库、新型数据分析方法和数据挖掘方法,得到氧化还原电位(ORP)与工艺过程可控因素间的可描述数学关系,为可控工艺参数的优化控制提供指导依据,进而提高生物冶金提金率。     

优化GD-模糊规则的滑坡预报模型研究

为了提高滑坡灾害预报准确率，改善传统的滑坡监测和预报中存在的参数选取困难及模糊控制系统作为预报模型精确度不高的问题，首先采用山体结构稳定性分析法进行滑坡灾害参数的选取，得出降雨量、含水率、土压力及岩土表面位移增量作为预报参数的结论；其次将选取的参数作为模糊系统的输入，建立滑坡灾害发生概率模型，并引入优化的GD算法修正预报模型中的动态参数，使模糊控制模型具有自适应性；同时与未优化的模糊控制模型以及单独模糊控制模型进行仿真对比，仿真结果表明，该控制算法收敛速度快，具有很好的收敛性；最后将该模型在某滑坡重点灾区实验区进行实验测试，实验结果显示该模型具有较好的收敛性，且预报精度达到90%。