Effects of bending curvature and text/background color-combinations of e-paper on subjects’ visual performance and subjective preferences under various ambient illuminance conditions

The study used the simulated e-paper to investigate how the bending radius of curvature (−10 cm, plane, and 10 cm) and 12 text/background color-combinations of e-paper affect subjects’ visual performance and subjective preference under various ambient illuminance conditions (200 and 500 lx). Analysis results indicated that the bending curvature and ambient illuminance did not significantly affect subjects’ visual performance. However, subjects visual performance differed significantly for different text/background color combinations of the simulated e-paper. When the background color of the simulated e-paper was set to yellow-like condition and the luminance of the text was low (2.2 and 4.6 cd/m²), subjects’ visual performance was best. Regarding the subjective preferences of subjects, the results of this research also demonstrated that the bending curvature, text/background color combinations and ambient illuminance all significantly affected the subjective preferences of subjects. Subjects exhibited the best preference under the following settings: bending curvature of the simulated e-paper set to plane; background color of the simulated e-paper set to yellow-like condition and low text luminance (2.2 or 4.6 cd/m²); high ambient illuminance (500 lx).     

Effects of computer icons and figure/background area ratios and color combinations on visual search performance on an LCD monitor

This study investigated the effects of figure/background color combinations, the type of computer icon and the figure/background area ratio on visual search performance on a liquid crystal display (LCD) screen. Participants had to search for the target item in a circle stimulus array, which had a diameter of 20 cm and included one target and 19 distracters. Results showed that these three independent variables significantly affected the visual search performance. The search times for EMAIL and SAVE computer icons were significantly shorter than that for a PRINT computer icon. Visual search performance was better for computer icons with a 90% figure/background area ratio than for a 70% ratio. However, no difference in search time between 90% and 50% ratios or between 70% and 50% ratios was observed. Color combinations also significantly affected the visual search performance and white/yellow and white/blue color combinations for the figure/background resulted in better performance than black/blue and black/yellow combinations. However, no difference in search time was observed for participants with and without esthetic training. The implications of the results are applicable to design icons for the interfacing of computers and communication information.     

Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments

Engineers have widely applied the Taguchi method, a traditional approach for robust experimental design, to a variety of quality engineering problems for enhancing system robustness. However, the Taguchi method is unable to deal with dynamic multiresponse owing to increasing complexity of the product or design process. Although several alternative approaches have been presented to resolve this problem, they cannot effectively treat situations in which the control factors have continuous values. This study incorporates desirability functions into a hybrid neural network/genetic algorithm approach to optimize the parameter design of dynamic multiresponse with continuous values of parameters. The objective is to find the optimal combination of control factors to simultaneously maximize robustness of each response. The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.     

Visual search experiment on text characteristics of vital signs monitor interface

In order to correctly read and interpret interface information shown on the interface of vital signs monitor, the challenging problems of misreading and misinterpreting the information must be addressed. Therefore, a visual search experiment that the text characteristics of the interface is carried out in this study to categorize parameterized information and encode parameterized numbers on a monitoring interface. The interface of a centralized vital signs monitor is simulated, and target searching of information objects is carried out in an experiment with three sets of characteristic variables: font size, background and text color, and spacing and location of the parameterized information. The experimental results show that the relative spacing of the information elements on the interface should be not too large or small. When the spacing is 30px, 38px, 46px, and 86px, the visual search process requires a long period time and it is easy to misinterpret the information. With an increase in the spacing, the plotted visual search response to different font sizes is a V-shape, while the rate of accuracy becomes an inverted V-shape. Compared to a font size 35pt and 55pt, a font size of 45pt results in the highest search efficiency and rate of accuracy. However, the background color has no significant effect on target searching in terms of reaction time. The findings show that the results in this study can be used as a guideline for the interface design of visual monitoring systems with diverse characters and spacing.     

Using N-D method to solve multi-response problem in Taguchi

Using Taguchi method to achieve a robust experimental design in the study of product quality is an important issue. The Taguchi method is to seek the best factors/levels combination with lowest societal cost solution to achieve customers requirements. However, the Taguchi method can only be used to optimize the single-response problem; it cannot be used to optimize the multi-response problem. This paper submits an optimal procedure, N-D method (Artificial Neural Network and Data Envelopment Analysis), by using artificial neural networks (ANNs) and data envelopment analysis (DEA) to achieve the optimization of multi-response problem. Two case studies in Su and Tong (1997) and Tong and Su (1997) are resolved by the proposed N-D method. The result deriving from the proposed N-D method indicates that it offers an efficient and feasible solution in the multi-response problems.This revised version was published in June 2005 with corrected page numbers.     

An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence

The Taguchi robust parameter design has been widely used over the past decade to solve many single-response process parameter designs. However, the Taguchi method is unable to deal with multi-response problems that are of main interest today, owing to increasing complexity of manufacturing processes and products. Several recent studies have been conducted in order to solve this problem. But, they did not effectively treat situations where responses are correlated and situations in which control factors have continuous values. This study proposed an integrated model for experimental design of processes with multiple correlated responses, composed of three stages which (1) use expert system, designed for selecting an inner and an outer orthogonal array, to design an actual experiment, (2) use Taguchi’s quality loss function to present relative significance of responses, and multivariate statistical methods to uncorrelate and synthesise responses into a single performance measure, (3) use neural networks to construct the response function model and genetic algorithms to optimise parameter design. The effectiveness of the proposed model is illustrated with three examples. Results of analysis showed that the proposed approach could yield a better solution in terms of the optimal parameters setting that results in a higher process performance measure than the traditional experimental design.     

Optimization of parameter design: An intelligent approach using neural network and simulated annealing

Parameter design optimization problems have found extensive industrial applications, including product development, process design and operational condition setting. The parameter design optimization problems are complex because non-linear relationships and interactions may occur among parameters. To resolve such problems, engineers commonly employ the Taguchi method. However, the Taguchi method has some limitations in practice. Therefore, in this work, we present a novel means of improving the effectiveness of the optimization of parameter design. The proposed approach employs the neural network and simulated annealing, and consists of two phases. Phase 1 formulates an objective function for a problem using a neural network method to predict the value of the response for a given parameter setting. Phase 2 applies the simulated annealing algorithm to search for the optimal parameter combination. A numerical example demonstrates the effectiveness of the proposed approach.     

集成环境下基于加工特征的工艺设计系统的研究

本文研究了ＣＩＭＳ集成环境下面向ＣＡＤ／ＣＡＭ的基于特征的工艺决策系统以及ＣＡＰＰ的基于加工表面的特征建模和工艺数据库的组织与应用。具体分析了工艺规划过程的特点，提出了ＣＡＰＰ系统应具有的功能模块及其要解决的主要技术问题。     

The effects of color combinations,luminance contrast,and area ratio on icon visual search performance

The visual search of icons in a digital interface plays an important role in human-computer interaction. In this behavior study about the visual search of graphic symbols, the effects of the color combination, luminance contrast, and icon area ratio were investigated. A total of 28 participants (12 men and 16 women, ages ranging from 20 to 28 years, Mean = 22.4, SD = 1.7) were asked to perform an icon search task. Participants’ icon search accuracy and response times were measured as a function of the following independent variables: three levels of area ratios, four background colors, four foreground colors, and three levels of luminance contrast. The results showed that although there was no significant main effect of luminance contrast on the icon search accuracy, participants responded more quickly to medium luminance contrast than low or high luminance contrast. Similarly, the medium or low area ratio was more conducive to the participants identifying icons. Moreover, a strong interaction was observed between the effects of luminance contrast and icon area ratio. The icon search performance of the high luminance contrast improved significantly as the figure/background area ratio decreased. Icon color combinations also significantly affected visual search performance. White on black, yellow on black, turquoise on black, white on red, yellow on red, and white on purple resulted in better performance than other color combinations. The findings of this study provide useful guidelines for designing more efficient and user-friendly icons and interfaces in the future.     

Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.     

Visual exploration of production data using small multiples design with non-uniform color mapping

Visual data mining may overcome some of the flexibility problem often suffered by computer-centered data mining approaches. This can happen because human beings are introduced to the information discovery loop to take advantage of their natural strength in creative thinking and rapid visual pattern recognition to discover information not defined a priori and to perform approximated reasoning that computer algorithms are hard to do. This paper presents a novel visual exploration approach for mining abstract, multi-dimensional data stored in tables in a relational database. The visual image is constructed by converting each table into a visualization unit called a table graph and then assembling these table graphs together to form a small multiples design. Different types of non-uniform color mappings to render this small multiples design could be automatically generated by minimizing the weight differences of colors in the visual image. These non-uniform color mappings are designed in such a way that the adjacent glyphs in a table graph that have near underlying values will be assigned with the same color. As such, visual patterns not able to see under the traditional uniform color mapping could be revealed. This enables the users to examine the input tables from different perspectives. The proposed flexible visualization method has been applied to generate visual images from which the users could quickly and easily compare the machine idle cost performances of alternative master production plans.     

网页视觉元素的情感设计

当今高技术的时代需要有离情感的环境,网站的情感设计是当下网站设计的重头戏,令人愉悦的外观能够大大提高网站的可用性。众多的视觉元素构成了整个网页的外观,而这些所有视觉元素的外观,又是由它们的色彩、形态和组合方式来形成的,从这三律,有助于我们对网站用户建立起良好的第一印象。给用户营造出高情感的网络环境。     

The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing

The paper proposes an approach to improve the yield of Chrome (Cr) thin-film sputtering process of the black matrix (BM) in color filter manufacturing and to find the robust parameters of the process with multiple quality characteristics by using the Taguchi method combined with the grey relational analysis. The research considers the correlation between quality characteristics and applies the principal component analysis to eliminate the multiple co-linearity. The weights of the quality characteristics are determined by employing the entropy measurement method. Two quality characteristics and four control factors with three levels were selected. Based on the Taguchi quality design concept, a L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics. Besides, manufacturing with the attained optimal process parameters can reduce the opportunities of repair and rework of Cr thin-film and raise the yield.     

基于PCCS色彩体系的ASD儿童干预APP配色研究

为提升自闭症谱系障碍(ASD)儿童干预APP的康复效果,为设计师提供合理有效的参考,提出一种基于PCCS色彩体系的ASD儿童干预APP界面配色评价方法。首先通过文献分析和焦点小组法,构建基于PCCS色彩体系的ASD儿童干预APP界面配色评价体系;其次运用AHP法获取各项评价指标的权重;再次筛选得到8个基于PCCS色彩体系的界面配色设计方案,邀请专家对其打分,得到初始评判矩阵;然后通过TOPSIS法获得各方案的排序结果;最后对结果进行解析。研究表明,ASD儿童对绿色、蓝色等冷色调及明度、纯度较高的色彩具有较高的认可度;在PCCS色调图中,ASD儿童更喜爱轻柔色调、浅色调、邻近色与互补色的搭配;在界面配色设计中应更趋于明快、清晰、和谐、稳重的色彩搭配。将该评价方法应用于干预APP界面配色实验方案评价中,可以提高设计评价过程中的客观性、科学性,为设计师进行决策提供参考,更好地促进ASD儿童干预APP的发展。     

A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method

Taguchi method is an efficient method used in off-line quality control in that the experimental design is combined with the quality loss. This method including three stages of systems design, parameter design, and tolerance design has been deeply discussed in Phadke [Quality engineering using robust design (1989)]. It is observable that most industrial applications solved by Taguchi method belong to single-response problems. However, in the real world more than one quality characteristic should be considered for most industrial products, i.e. most problems customers concern about are multi-response problems. As a result, Taguchi method is not appropriate to optimize a multi-response problem. At present, it is still necessary to rely on the engineering judgment to optimize the multi-response problem; therefore uncertainty will be increased during the decision-making process. On the other hand, due to some uncontrollable causes occurring, only a portion of experiment can be completed so that the censored data will be produced. Traditional approaches for analysis of censored data are computationally complicated. In order to overcome above two shortages, this article proposes an effective procedure on the basis of the neural network (NN) and the data envelopment analysis (DEA) to optimize the multi-response problems. A case study of improving the quality of hard disk driver in Su and Tong [ Total Quality Management 8 (1997) 409] is resolved by the proposed procedure. The result indicates that it yields a satisfactory solution.     

Solving the multi-response problem in Taguchi method by benevolent formulation in DEA

The Taguchi method is an efficient approach for optimizing a single quality response. In practice, however, most products/processes have more than one quality response of main interest. Recently, the multi-response problem in the Taguchi method has gained a considerable research attention. This research, therefore, proposes an efficient approach for solving the multi-response problem in the Taguchi method utilizing benevolent formulation in data envelopment analysis (DEA). Each experiment in Taguchi’s orthogonal array (OA) is treated as a decision making unit (DMU) with multiple responses set inputs and/or outputs. Each DMU is evaluated by benevolent formulation. The ordinal value of the DUM’s efficiency is then used to decide the optimal factor levels for multi-response problem. Three frequently-investigated case studies are adopted to illustrate the proposed approach. The computational results showed that the proposed approach provides the largest total anticipated improvement among principal component analysis (PCA), DEA based ranking approach (DEAR) and other techniques in literature. In conclusion, the proposed approach may provide a great assistant to practitioners for solving the multi-response problem in manufacturing applications on the Taguchi method.     

Visual search in vibration environments: Effects of spatial ability,stimulus size and stimulus density

Visual search is one type of common encountered human-computer interaction tasks, but it has less been examined in vibration environments. The aim of this study was to examine the effects of one user characteristic (spatial ability) and two stimulus characteristics (i.e., stimulus size and stimulus density) on visual search performance during single- and double-target tasks in simulated vibration environments. Twenty-four participants attended an experiment where they were instructed to perform single- and double-target visual search tasks with varied levels of stimulus size and stimulus density under static, slight and moderate vibration environments, respectively. Results indicated that the two vibration conditions achieved comparable visual search performance, perceived visual fatigue, and perceived comfort with static condition across task types. Stimuli size and stimulus density yielded significant effects on visual search time in both single- and double-target tasks (p's < 0.001). Participants with high spatial ability were faster than those with low spatial ability in double-target tasks (p = 0.042). Users preferred interface design with medium-to-large stimulus size and low-to-medium stimulus density in visual search.     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming,quality loss functions and particle swarm optimization

The lighting performance of a 3535 packaged hi-power LED (light-emitting diode) is mainly influenced by its geometric design and the refractive properties of its materials. In the past, engineers often determined the settings of the geometric parameters and selected the refractive properties of the materials through a trial-and-error process based on the principles of optics and their own experience. This procedure was costly and time-consuming, and its use did not ensure that the settings of the design parameters were optimal. Therefore, this study proposed a hybrid approach based on genetic programming (GP), Taguchi quality loss functions, and particle swarm optimization (PSO) to solve the multi-response parameter design problems. The feasibility and effectiveness of the proposed approach was demonstrated by a case study on improving the lighting performance of an LED. The confirmation results showed that all of the key quality characteristics of an LED fulfill the required specifications, and the comparison found that the proposed hybrid approach outperforms the traditional Taguchi method in solving this multi-response parameter design problem. The proposed hybrid approach can be extended to solve parameter design problems with multiple responses in various application fields.     

基于田口法的产品质量工程优化系统开发

产品质量工程是提高产品质量与可靠性、降低研制成本、缩短研制周期的重要途径,是目前研究的热点,田口法是它的核心技术;该文简要介绍了田口法的核心理论一在线与脱线质量工程理论,质量损失模型和分析工具(信噪比和正交试验设计);基于田口法,以JAVA编程语言为工具,对田口法的脱线质量工程进行了可视化应用软件系统的开发;本文介绍了该软件系统的作用、特点及运行过程,并对软件的系统功能模块和总体设计作了详细说明,最后指出了本软件的开发方向。