A systematic approach for coupling user satisfaction with product design

A fundamental assumption in this paper is that user satisfaction depends on product design. The approach consists of 5 steps: (1) define user satisfaction, (2) decompose product design elements, (3) conduct experiments, (4) develop relationship models, and (5) analyse critical design features. In order to demonstrate the practicability of this approach, relationship models were developed based on experimental data using a total of 60 subjects (30 American and 30 Korean subjects). In addition, critical design features and their common properties were identified for audio/visual consumer products. Similarities and differences between American and Korean consumers were discussed. The resulting relationship models can be used to predict user satisfaction and provide significant remedies for design change.     

The rapid selecting of precursor ions and product ions of thirty-four kinds of pesticide for content determination by GC–EI/MS/MS

Pesticide residues caused great threat to human body health, and all countries protected the health of human body by specifying its upper limit. However, pesticide residues were generally very low, similar to trace analysis and as low as millionth or less, the content determination of pesticide residues was a troublesome problem. With the application and promotion of tandem mass spectrometry, it was able to do trace analysis. GC–EI/MS/MS was a common method for the determination of pesticide residues. The multiple reaction monitoring (MRM) mode was the most common quantitative method used in GC–EI/MS/MS. It has the characteristics of high sensitivity, good reproducibility, high accuracy, strong anti-interference and high ion flux. Multiple-twin precursor ions, product ions and collision energy of thirty-four pesticides were provided for MRM. It could play an important role in developing MRM method for the quantifying of 34 kinds of pesticide. Meanwhile, the development of MRM method for the quantifying of other compounds could also refer to this paper.     

Florence Duomo project (1420–1436): Learning best project management practice from history

This paper narrates the project management of the construction of the Florence Duomo by Filippo Brunelleschi in the fifteenth century. This was the most significant dome project in Europe in 1300 years, and possibly the most significant, innovative and complex project of the Renaissance era (Colombo and Lanzavecchia, 1997). It still stands as the largest brick dome ever built. In order to achieve what seemed technically impossible at the time, Brunelleschi researched and adapted the construction and project management of the Pantheon in Rome in the second century. The paper allows us in turn to learn both product and process innovation from this case study, both of which are essential to contemporary project management practice. The case is valuable in understanding key drivers of project management success, and illustrates the substantial potential for learning, and therefore knowledge transfer, from previous historical projects and experiences.     

Model predictive control suitable for closed-loop re-identification

The main problem of a closed-loop re-identification procedure is that, in general, the dynamic control and identification objectives are conflicting. In fact, to perform a suitable identification, a persistent excitation of the system is needed, while the control objective is to stabilize the system at a given equilibrium point. However, a generalization of the concept of stability, from punctual stability to (invariant) set stability, allows for a flexibility that can be used to avoid the conflict between these objectives. Taking into account that an invariant target set includes not only a stationary component, but also a transient one, the system could be excited without deteriorating the stability of the closed-loop. In this work, a MPC controller is proposed that ensures the stability of invariant sets at the same time that a signal suitable for closed-loop re-identification is generated. Several simulation results show the propose controller formulation properties.     

Application of statistical modeling of image spatial structures to automated visual inspection of product quality

Automated visual inspection (AVI) attracts increasing interest in product quality control both academic and industrial communities, particularly on mass production processes, because product qualities of most types can be characterized with their corresponding surface visual attributes. However, many product images in AVI systems are comprised of stochastically accumulative fragmentations (particles) of local homogeneity, without distinctive foregrounds and backgrounds, which brings great challenges in computer analysis, e.g., rice images, fabric images, and consequently, in the intelligent identification of the product qualities. A method of Weibull distribution (WD)-based statistical modeling of image spatial structures (ISSs) to inspect automatically the product quality is presented. The ISS, obtained with multi-scale and omnidirectional Gaussian derivative filters (OGDFs), was demonstrated to be subject to a representative WD model of integral form based on the theory of sequential fragmentation in advance. The WD-model parameters (WD-MPs) of the ISS, with essential human perceptual significance, were extracted as the visual features for product quality identification. The classification performance of the proposed product quality inspection method, namely, the proposed WD-MP features integrated with an introduced spline regression (SR) classifier in this study, was verified on two case studies in the field of the AVI of product quality, namely, automated rice quality classification, and intelligent fabric quality assessment in the corresponding assembly lines of industrial scale. Experimental results indicate that the proposed WD-MP features can effectively characterize the statistical distribution profiles of ISS of complex grain images, piled with a large number of stochastically accumulative fragmentations. The proposed method provides an effective tool for grain image modeling and analysis and consequently lays a foundation for the intelligent perception of product qualities on assembly lines.     

Trained sensory panellists’ response to product alcohol content in the projective mapping task: Observations on alcohol content,product complexity and prior knowledge

Projective mapping has been validated as a practical tool for the rapid sensory profiling of brandy products, although repeatability concerns necessitate repeated measurements in larger sample sets. The reason for poor repeatability could be linked to the complexity of the product type, as well as the physical and possibly psychological factors associated with its high alcohol content. To date no information has been published that tested the effect of these specific factors on panellist performance in projective mapping tasks. This study tested the effect of sample complexity and alcohol content on sensory panel repeatability and accuracy in projective mapping, using six types of commercial alcoholic beverages. In a second objective, the study also tested the effect of prior knowledge of alcohol content of a given product set on panellist performance in projective mapping. The results showed that complexity had the biggest impact on panel performance, while alcohol content had a secondary but decisive influence, largely due to its chemosensory fatiguing nature. Knowledge of the product alcohol content appeared to affect individuals differently, and also had an effect on the terminology used by the panellists to describe the products. The study also introduces the Relative Performance Indicator (RPI) as a new panel performance monitoring tool for projective mapping.     

Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overview

Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.     

Mathematical modelling and parameter identification of a stainless steel annealing furnace

A new, comprehensive mathematical model of continuous annealing furnaces is developed, under consideration of both the radiative and convective heat transfer of the furnace components. Based on measured normal operating data from an industrial stainless steel plant, parameter identification is basically carried out using a nonlinear least-squares optimization algorithm for the whole annealing furnace, to estimate optimal values of uncertain parameters, such as emissivities. Due to the complexity of the model, a sequential approach for parameter identification is proposed and implemented, i.e. the parameter set is divided into different subsets, and the parameter estimation is carried out sequentially in several steps and iterations. The performance of the model with the estimated parameters is then evaluated on a different test data set. It is shown that the obtained model can predict temperature evolutions along the furnace in good agreement to measured data, under both steady-state and transient conditions. The presented model is suitable for controller design and process optimization.     

基于软件身份标签的安全性研究

当前,由于软件发行商通常不为应用程序和可执行文件（程序文件,共享库,脚本等）提供用以验证其可靠性的关键信息,所以计算机管理员想要自动辨别这些文件是否被第三方修改过变得十分困难.通过与民航飞行的安全性要求进行类比,分析了软件身份标签如何从身份鉴别、应用关联、防止篡改和未知威胁这四方面改善软件的安全性.     

Italian market fish species identification and commercial frauds revealing by DNA sequencing

To guarantee consumers about the security of what they eat is the main goal of all the institutions working to assure food safety. In seafood field it appears of prime importance to be able to identify fish species throughout the production cycle also when the product has been already processed (breaded fish, fillets etc). In this context the latest system proposed for species identification is the so called “DNA-Barcoding”, made by carrying out the DNA sequencing of a standard barcode region. In the present study we carried out bi-directional sequence analysis of mitochondrial cytochrome b gene (Cytb) and cytochrome oxidase subunit I gene (COI). The main goal of the research was to unambiguously classify the most common fishes traded in Italy, being able to ascertain possible labeling frauds made substituting value species with less precious ones. With both genes we could correctly identify almost all the samples in study: respectively 58/58 with COI and 56/58 with Cytb. This study confirms mitochondrial genes Cytb and COI as good candidates for fish species identification by DNA sequencing. This method appears particularly suitable especially when morphological characterization is difficult, (for very close species), or impossible (for transformed foodstuffs) to carry out. What's more we could efficiently identify many fishes belonging to the Mediterranean fauna, which, to our knowledge, underwent for the first time this kind of analysis. Further development will concern the extension of the analysis to a largest number of local species.