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Emotional cellular (EC), proposed in our previous works, is a kind of semantic cell that contains kernel and shell and the kernel is formalized by a triple-

Natural language (NL) is used to describe the real world while NL is full of fuzziness and ambiguity, humans will create a vague concept on each object by using NL, whereas researching on implicit emotion of product style, NL are often unable to adapt to the quantitative calculation and qualitative analysis. Customer's emotional evaluation is hided in NL that needs to be extracted and decomposed into words and adjectives, but separating these terms under such a simple way cannot fully express the human's visual perception of product. In previous studies, it implicated that human emotion can be measured by two-dimensional value, called valence and arousal, where valence denotes the degree of exciting and calm and arousal denotes the degree of positive and negative [

Emotional cellular (EC), introduced by our previous works, was developed based on Labe semantic (LS) and prototype theory (PT) [

case-based Reasoning (CBR) refers to use of existing experience under a suitable similarity definition to find a solution to the problem, or revise the past cases while making decisions on new case. The advantage of CBR is that system does not need to enter the entire cases in advance, but simply to rely on the reasoning process comparing with current problems and past cases that have more application on text inference and pattern recognition fields [

While in actual process, CBR method must be combined with fuzzy sets that will take more effective for feature extraction of product style due to the uncertainty on knowledge representation of the products style, form features, attributes description, and the definition of similarity measurement; so fuzzy case-based reasoning (FCBR) was introduced subsequently [

In the 1980s, Schank [

CBR emphasizes the connection between cases and makes it more feasible for researching the formation of product style. In conceptual design (CD) fields, Cheng et al. [

Case-based reasoning has been formalized as a four-step process [

Retrieve: retrieve cases from memory, that are, relevant to solving a target problem. A case consists of a problem that how the solution was derived.

Reuse: the solution from the previous case was mapped to the target problem for that it need to involve adjust the previous solution to fit the new situation.

Revise: test new solution, if necessary; revise it after having mapped the previous solution to the target situation.

Retain: store the resulting experience as a new case in memory after the solution has been successfully adjusted to the target problem (Shown as Figure

Process of general case-based reasoning.

Fuzzy logic can deal with the expression of fuzzy language better, such as, “very good,” “good,” “bad,” and “very bad.” Combining with fuzzy sets to CBR process, a fuzzy preference function was defined to calculate the similarity between the corresponding properties for target problems. The result of fuzzy preference function is a vector, called the fuzzy preference vector (FPV) while the vector contains the fuzzy preference value for each attribute; extracting fuzzy preference function allows comparing some particular characteristic under completely different scales.

Nearest-neighbor (NN) technology was used to calculate the similarity between cases, comparing the relative properties for each new case and previous cases under given case weights, the total weight (weighted sum) was calculated and applied to sort the candidate cases. Most of the CBR systems use this method, and the degree of similarity may often be normalized into a number between 0 and 1, where 0 denotes completely different, 1 denotes exactly same, or as a percentage while 100% is representative of exactly same. This theory was first introduced by Fix and Hodge [

In the investigation stage, product style features need to be described more closely to natural language. In previous studies, the usual practice was to use linguistic variables (LV); LV is simplify of natural language and also combined fuzzy set theory. Table

Linguistic variable and its scale.

Scale | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

Variables | 2 | 3 | 5 | 5 | 6 | 7 | 9 | 11 |

None-N, N | Y | |||||||

Very low, VL | Y | Y | Y | Y | Y | |||

Low-very low, V-VL | Y | |||||||

Low, L | Y | Y | Y | Y | Y | Y | Y | Y |

Fairy low, FL | Y | Y | Y | Y | ||||

Mol low, ML | Y | Y | ||||||

Medium, M | Y | Y | Y | Y | Y | Y | ||

Mol high, MH | Y | Y | ||||||

Fairy high, FH | Y | Y | Y | |||||

High, H | Y | Y | Y | Y | Y | Y | Y | Y |

High-very high, H-VH | Y | |||||||

Very high, VH | Y | Y | Y | Y | Y | |||

Excellent, E | Y |

Fuzzy nearest neighbor (FNN), first proposed by Keller et al. [

While properties of each case may represent different importance degrees, we must set each weight relative to a property in order to find the most similar case more effectively and accurately. For NN algorithm technology, the distribution of weights will help us to acquisition “good” case. However, value selection for weight of property is very difficult, traditionally, weights are defined by experts according to their expertise or experience in practice.

Combining with FNN and CBR by using discrete scale-LV cannot be more exact to describe product style, so the EC model needs to be used in this reasoning system. By the above discussion and analysis, the similarity formula for cases is also redefined.

In a previous experimental study [

For

Distance

for

Single-point kernel is defined by

Circular kernel is defined by

Flat kernel is defined by

The shell of EC gives the scale of neighborhood of all emotional elements that is uncertainty called “soft membrane” which is defined by the following.

Upper approximation shell:

For singular- and circular-type kernel of EC, single Gaussian model (SGM) was applied to describe the density of these points, defined by

Different parameters in EC models also are subject to density function requirements. (a) Different

And for plat-type kernel of EC, multi-SGMs will be addressed to resolve this problem,

Overlapping appearance of different type kernel of EC,

Firstly, we need to define the target problems, while using EC, we have the following.

Given a universe

Given a product features space

For any two style feature subset

Let

Construct a mapping between product feature space and case space-based EC:

For case base

The distance between form feature

let

Let case base

Style features subset calculation under fuzzy expression for new case:

For new case

The frame works of FCBR process was illustrated by Figure

Similarity computing for new case in valence-arousal emotional space.

The mobile phone was classified under form, layout (geometric connection), color, and material. In form feature classification, there have been 8 subclassifications: let

Form classification and feature coding.

Class | Form features |
---|---|

{ | |

{ | |

{ | |

{ | |

{ | |

{ | |

{ | |

{ |

In layout classification, there have been 6 sub-classifications, let (

Geometric connections and feature coding.

Class | Form features |
---|---|

{ | |

{ | |

{ | |

{ | |

{ | |

{ |

In color and materials classification, there have been four subclasses: let (

Color and materials and their codes.

Class | Form features |
---|---|

{ | |

{ | |

{ | |

{ |

In color evaluation, 2-dimensional scale was applied in system shown in Table

2-dimensional color style evaluation (“Y”-selected).

Words | A | B | C | D | E | F | |

Fortunately | Active | Varied | Lucky | Interesting | Peaceful | ||

1 | Optimistic | ||||||

2 | Stimulation | Y | |||||

3 | Sexy | Y | |||||

4 | Intense | Y | |||||

5 | Aggression | Y | |||||

6 | Gorgeous | ||||||

7 | Lively | Y | |||||

8 | Elegant | Y | |||||

9 | Rich | Y | |||||

10 | Mature | ||||||

11 | Healing | Y | |||||

12 | Wisdom | ||||||

13 | Cool | ||||||

14 | Protection | ||||||

15 | Security | Y | |||||

16 | Loyalty | Y |

Let

78 mobile phones were used to construct case base through online questionnaire system. First, these 78 Nokia mobile phones will be formalized as (

Style description, representative product and kernel features codes (Nokia).

Style | Product | EC characterize code | |
---|---|---|---|

Classical | N93 | C3E2I1M1 | |

Aristocratic | 8800 | E3G1M2R4Q2 | |

Rock | N70 | A1B2M3 | |

Dynamic Black | N81 | B3E1G4Q3R6 | |

Adore | 7088 | A3E2G2K4Q7 | |

Metal | 6275 | E2I1M2Q8 | |

Classic Nordic | E50 | B3C1D2M3 | |

Bohemian | 7373 | G3Q6 | |

Luxury | 7500 | D2H5J2Q3 | |

Apple iPod | N73 | G1I3M2Q7 | |

Simple business | E51 | I3O3P2R7 | |

Modern fashion | 7900 | D2H4P2 | |

Sport Transformation | 5300 | H2M2 | 4B6D |

Comparing with traditional methods, such as KANSEI engineering, it applied KANSEI adjectives on perceptual evaluation and also needed the appropriate degree of adverbs to indicate customers’ preference on every product characteristics, such as “very”and “extreme”. However, this scale has great randomness and cannot be directly used for calculation; further improvement is Likert scale that used psychology to reflect the preference. It used scores to distinguish emotional level such as 5-point scale and 7-point scale; scores also can distinguish the corresponding positive or responsible for the emotional level, but the scale method is far too simple. The use of semantic differential method still does not solve the complex emotional quantitative problem, and also cannot reflect the perception of ambiguity. In this respect, fuzzy set was considered to be an effective solution. In the fuzzy set theory, the user is represented as a composite perceptual general form of fuzzy sets, and with the logical calculation on fuzzy set, the user’s overall emotional evaluation on products’ implicit emotion would be extracted, the methodology combining genetic algorithm was adopted in our works on TV style research [

The form, color, and materials of product played a decisive role in the formation of product style by researching expression and reasoning of style knowledge; due to the uncertainty of product style, FCBR was addressed to resolve this problem and product style was formalized as an EC, the similarity between cases was also defined from EC’s probability density function, in the case study, this model was proved to be effective, but, it had also the following problems.

Similarity computing in the style of the weight of morphological characteristics’ calculation is too complex while depend on probability density function of EC that limit the size of the experiment, and the accuracy of this density function remains to be further studied.

Besides the form, layout, color, and materials, there are other factors, such as the use of functional brand recognition product emotional experience will also influence the product style formation. Therefore, the extraction of knowledge of product style was improved and developed continuously.

In the next step, case base will be established in a large scale while similarity computing must be under more reasonable definitions, weight distribution program, and inference system also would be improved, that is to carry out the depth mining for product style.

This paper is funded by Natural Science Foundation of Zhejiang Province under Grant no. Y1110322 and National Natural Science Foundation under Grant no. 61070075.