Product design enhancement using apparent usability and affective quality

In this study, apparent usability and affective quality were integrated in a design framework called the Usability Perception and Emotion Enhancement Model (UPEEM). The UPEEM was validated using structural equation modeling (SEM). The methodology consists of four phases namely product selection, attribute identification, design alternative generation, and design alternative evaluation. The first stage involved the selection of a product that highly involves the consumer. In the attribute identification stage, design elements of the product were identified. The possible values of these elements were also determined for use in the experimentation process. Design of experiments was used to identify how the attributes will be varied in the design alternative stage and which of the attributes significantly contribute to affective quality, apparent usability, and desirability in the design evaluation stage. Results suggest that product attributes related to form are relevant in eliciting intense affect and perception of usability in mobile phones especially those directly related to functionality and aesthetics. This study considered only four product attributes among so many due to the constraints of the research design employed. Attributes related to aesthetic perception of a product enhance apparent usability such as those related to dimensional ratios.     

An image evaluation approach for parameter-based product form and color design

The parameter-based technique provides an efficient and valid means of constructing 3-D geometric models in many CAD software systems. However, its use is generally restricted to the design of mechanical components with regular configurations, and it is not ideally suited to product form and color design. This paper proposes a rapid conceptual design approach, which creates color-rendered forms and combines parameter-based features with fuzzy neural network theorems and gray theory to predict their image evaluation. Two evaluation models (Evaluation Model I and Evaluation Model II) are developed and applied in a case study of an electronic door lock design. Model I uses a fuzzy neural network to predict the overall image, while Model II uses a gray clustering operation for the color image evaluation and two fuzzy neural networks for the form image evaluation and the overall image evaluation. The results show that the image prediction capability of Model II is superior to that of Model I (RMSE: 0.062 versus 0.105). Furthermore, the overall image evaluation is dominated by the door lock's color rather than by its form (RMSE: 0.071 versus 0.162). The dominance of color in determining the image evaluation may be due to the specified image words, form evolution restrictions, or the membership grade ranges of the test color samples and the test form samples, etc. Having established the superiority of Model II, it is applied to develop a consultative design interface integrated with a professional CAD system in order to demonstrate the effectiveness of the proposed product design and image evaluation approach. The design system presented in this study enables a designer to predict the likely image tendencies of a designed product without the need to create and test a prototype model. Hence, he or she can make any design parameter modifications necessary to ensure that the finished product meets its specified image goals.     

Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving.     

A support vector regression based prediction model of affective responses for product form design

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers’ affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.     

A study on the preliminary ship design method using deterministic approach and probabilistic approach including hull form

This paper describes a preliminary ship design method using deterministic approach and probabilistic approach in the process of hull form design. In the deterministic approach, an interdisciplinary ship design method integrates principal dimension decisions and hull form variations in the preliminary ship design stage. Integrated ship design, as presented in this paper, has the distinctive feature that these parameters are evaluated simultaneously. Conversely, in sequential design, which is based on the traditional preliminary ship design process, hull form designs and principal dimension decisions are determined separately and sequentially. The current study adopts the first method to enhance the design quality in the early design stage. Furthermore, a probabilistic approach is applied to ship design to resolve uncertainties in design information more efficiently than a deterministic approach would.     

Turbine stage design aided by artificial intelligence methods

We propose a general, efficient system for designing turbine cascades and stages in real 3D-flow conditions. The presented algorithms involve application of evolutionary algorithms, as well as Artificial Neural Networks. Results of the design process are shown to be highly optimised in terms of efficiency, whereas computation time is reduced by several orders of magnitude in comparison to methods relying on Computational Fluid Dynamics calculations.     

Automatic design support and image evaluation of two-coloured products using colour association and colour harmony scales and genetic algorithm

Colour plays a key role in determining a consumer’s response to a product’s appearance. Accordingly, this study proposes an automatic design support system which enables a designer either to emulate the colour scheme of a two-coloured product and then to determine its corresponding image perception, or to search for the two-colour combination which most closely meets the required image perception. The proposed system combines a gray theory-based colour-association evaluation method and a colour-harmony-based aesthetic evaluation method to design and evaluate different product-colour schemes. Since colour-harmony theories cannot be implemented directly using the additive primaries (i.e. R (Red), G (Green) and B (Blue)) used in most computer-based colour emulations, this study develops a RGB-based colour-association and colour-harmony measurement scheme to evaluate the image perception of a particular product-colour scheme. In an inverse process, a genetic algorithm is applied to search for the near-optimal colour combination which satisfies the specified product colour-association goal and achieves a high degree of colour harmony. Various case studies involving the design and evaluation of a two-coloured thermos flask are provided for illustration purposes to demonstrate the effectiveness of the proposed method.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.     

Enhanced customer requirement classification for product design using big data and improved Kano model

The classification of customer requirements (CRs) has a significant impact on the solution of product design. Existing CRs classification methods such as the Kano model and IPA model are time-consuming and inaccurate. This paper proposes a CRs classification method for product design using big data of online customer reviews of products to classify CRs accurately and efficiently. Comments of customer reviews are matched to CRs using a hierarchical semantic similarity method. Customer satisfaction degrees are defined based on emotional levels of adjectives and adverbs of customer comments using word vectors. The function implementation degree of each product is determined by specifications crawled from online products. Fitting curves are formed by defined customer satisfaction and function implementation of CRs using polynomial modeling and least square methods. Based on the slope of the fitted curves, CRs are classified to provide the minimum and maximum function implementations of CRs in each CR group to guide a product design process. The proposed method is applied in a case study of defining CRs classifications for design of upper limb rehabilitation devices. For verifying the proposed method, CRs defined by the existing methods are compared with CRs from the proposed method in design of an upper limb rehabilitation device.     

The modified fuzzy art and a two-stage clustering approach to cell design

This study presents a new pattern recognition neural network for clustering problems, and illustrates its use for machine cell design in group technology. The proposed algorithm involves modifications of the learning procedure and resonance test of the Fuzzy ART neural network. These modifications enable the neural network to process integer values rather than binary valued inputs or the values in the interval [0, 1], and improve the clustering performance of the neural network. A two-stage clustering approach is also developed in order to obtain an informative and intelligent decision for the problem of designing a machine cell. At the first stage, we identify the part families with very similar parts (i.e., high similarity exists in their processing requirements), and the resultant part families are input to the second stage, which forms the groups of machines. Experimental studies show that the proposed approach leads to better results in comparison with those produced by the Fuzzy ART and other similar neural network classifiers.     

Auto-tune system using single-run relay feedback test and model-based controller design

In this paper, a systematic approach for auto-tune of PI/PID controller is proposed. A single run of the relay feedback experiment is carried out to characterize the dynamics including the type of damping behavior, the ultimate gain, and ultimate frequency. Then, according to the estimated damping behavior, the process is classified into two groups. For each group of processes, model-based rules for controller tuning are derived in terms of ultimate gains and ultimate frequencies. To classify the processes, the estimation of an apparent deadtime is required. Two artificial neural networks (ANNs) that characterize this apparent deadtime using the ATV data are thus included to facilitate this estimation of this apparent deadtime. The model-based design for this auto-tuning makes uses of parametric models of FOPDT (i.e. first-order-plus-dead-time) and of SOPDT (i.e. second-order-plus-dead-time) dynamics. The results from simulations show that the controllers thus tuned have satisfactory results compared with those from other methods.     

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.     

Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage

Accurately predicting tidal levels, including tidal and freshwater discharge effects, is important for human activities in estuaries. The traditional harmonic analysis method and numerical modeling are usually adopted to simulate and predict estuary water stages. This study applied artificial neural networks (ANNs) as an alternative modeling approach to simulate the water stage time-series of the Danshui River estuary in northern Taiwan. We compared this approach with vertical (laterally averaged) 2D and 3D hydrodynamic models. Five ANN models were constructed to simulate the water stage time-series at the Shizi Tou, Taipei Bridge, Rukuoyan, Xinhai Bridge, and Zhongzheng Bridge locations along the Danshui River estuary. ANN models can preserve nonlinear characteristics between input and output variables and are superior to physical-based hydrodynamic models during the training phase. The simulated results reveal that the vertical 2D and 3D hydrodynamic models could not capture the observed water stages during an input of high freshwater discharge from upstream boundaries, while the ANN could match the observed water stage. However, during the testing phase, the ANN approach was slightly inferior to the 2D and 3D models at the Xinhai Bridge, Zhongzheng Bridge, and Rukouyan locations. Our results show that the ANN was able to predict the water stage time-series with reasonable accuracy, suggesting that ANNs can be a valuable tool for estuarine management.     

Least-cost design of singly and doubly reinforced concrete beam using genetic algorithm optimized artificial neural network based on Levenberg–Marquardt and quasi-Newton backpropagation learning techniques

In this work, least-cost design of singly and doubly reinforced beams with uniformly distributed and concentrated load was done by incorporating actual self-weight of beam, parabolic stress block, moment–equilibrium and serviceability constraint besides other constraints. Also, this design expertise was incorporated into a genetically optimized artificial neural network based on steepest descent, Levenberg–Marquardt, and quasi-Newton backpropagation learning techniques. The initial solution for the optimization procedure was obtained using limit state design as per IS: 456-2000.     

Integrating engineering design and analysis using a multi-representation approach

With the present gap between CAD and CAE, designers are often hindere in their efforts to explore design alternatives and ensure product robustness. This paper describes the multi-representation architecture (MRA)—a design-analysis integration strategy that views CAD-CAE integration as an information-intensive mapping between design models and analysis models. The MRA divides this mapping into subproblems using four information representations: solution method models (SMMs), analysis building blocks (ABBs), product models (PMs), and product model-based analysis models (PBAMs). A key distinction is the explicit representation of design-analysis associativity as PM-ABB idealization linkages that are contained in PBAMs.The MRA achieves flexibility by supporting different solution tools and design tools, and by accommodating analysis models of diverse discipline, complexity and solution method. Object and constraint graph techniques provide modularity and rich semantics.Priority has been given to the class of problems termedroutine analysis—the regular use of established analysis models in product design. Representative solder joint fatigue case studies demonstrate that the MRA enables highly automated routine analysis for mixed formula-based and finite element-based models. Accordingly, one can employ the MRA and associated methodology to create specialized CAE tools that utilize both design information and general purpose solution tools.Nomenclature MRA multi-representation architecture - SMM solution method model - ABB analysis building block - PM product model - PBAM product model-based analysis model - ABB-SMM transformation - idealization relation between design and analysis attributes - PM-ABB associativity linkage indicating usage of one or more _i eislab. eislab. gatech. edu.     

Generation and evaluation of product concepts by integrating extended axiomatic design,quality function deployment and design structure matrix

Existing product concept generation and evaluation methods are mainly based on designers' experience to determine design schemes in the process of product development, which is time-consuming and ineffective. This paper proposes an approach to generate and evaluate design concepts by integrating the extended axiomatic design, quality function deployment and design structure matrix. Different design domains are mapped for matrix operations to generate feasible concepts based on design criteria. A domain mapping matrix is built to determine technical measures, functional requirements and design parameters based on customer requirements. The proposed approach provides a structured method to quantify, validate and qualify design concepts. A case study of the design of a hand rehabilitation device demonstrates the effectiveness of the proposed method.     

基于遗传算法优化神经网络的产品造型设计评价

为全面科学地评定、筛选最佳设计方案,借助遗传算法的全局寻优能力对BP神经网络进行优化,构建混合GA‐BP算法,并将其应用于笔记本产品造型设计评价中。在建立笔记本造型二级指标评价体系的基础上,利用18款设计方案中的15款对混合GA‐BP评价系统进行训练,利用其余3款设计方案对训练后的系统进行验证。验证结果表明,模拟值与实际值的相对误差分别为3?6％、－1?7％和2?8％,显示了较高的精度,反映了混合GA‐BP产品造型设计评价系统能对设计方案进行科学的评价。     

A new approach for product evaluation based on integration of EEG and eye-tracking

Exploiting biometric measures, especially neurophysiological data of evaluator for product evaluation is advantageous at avoiding bias and subjectivity in expert scoring process. This paper proposes an approach that integrates electroencephalograph (EEG) and eye-tracking (ET) data in a new way to derive multi-faceted supportive information for product evaluation. Firstly, emotion recognition from EEG signals of evaluator is carried out with a spatial–temporal neural network. Then, based on correlations between emotions and preferential judgement, general customer preference toward product design scheme is inferred from emotions by fuzzy system. Finally, general preference is integrated with ET data at application-level to quantify fine-grained customer preferences toward design modules and visual attractiveness. This approach is verified with a case study which evaluates six designs of frontal area of automotive interior, and valuable supportive information for design decision-making is yielded. Also, comprehensive analysis is conducted and the results verify the effectiveness of proposed approach.     

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.