本研究建立了一套以感性工學為基礎的毛線織片分析流程，結合類神經網路將消費者對毛線織片的意象感覺反應在設計師的設計作品上，藉以加速設計的流程，輔助設計師能夠以較有效率且客觀的方式進行織片設計。紡織業在台灣是一個很成熟的產業，由於科技進步、技術發達、消費者意識的抬頭，加速了產品的生命週期，如何幫助設計師掌握消費者的感覺，藉以提升產品創新的時效性，是一項不容忽視的問題。

本研究利用明大廠商提供的毛線織片樣本 50 片，採問卷方式請受測者從 10 組視覺加觸覺(視觸覺)、10 組觸覺感性語彙對織片樣本進行評分。撰寫程式擷取出樣本影像的色彩黏著度當作其色彩特徵，LBP（局部二位元圖形，local binary pattern）、SCOV、SAC、VAR等分析影像灰階值彼此相關性的方法進行影像紋理特徵的擷取，並將廠商所提供織片的織法與材質資料加以編碼。以色彩特徵值、紋理特徵值、織法與材質編碼為輸入層，感性語彙得分為輸出層，以45個織片樣本進行倒傳遞類神經網路訓練。以5個測試織片樣本進行測試，發現以VAR紋理特徵加織法與材質編碼為輸入層的視觸覺類神經網路驗證結果，在視觸覺感性值的預測有很高的準確性，LBP紋理特徵加材質與織法編碼為輸入層的觸覺類神經網路驗證結果在觸覺感性值的預測有很高的準確性。藉由訓練完成的類神經網路，設計師能根據新樣本的色彩特徵值、紋理特徵值、織法與材質編碼而計算出新的感性意象值，如此可提高設計師在毛線織片設計的效率與水準。

This research established a set take the Kansei Engineering as the foundation flow, and combined neural network to addresses the issue of how humans perceive to the yarn and response on the designers’ new works, so as to the acceleration design flow assisted the designer able to carry on design by more effective also an objective way. The textile industry was already the very mature industry in Taiwan's, because the advance in technology, the technology were developed gaining ground which, the consumer realized, has accelerated the product life cycle, formerly admired the artificial platoon regulation the way because the effectiveness for a period of time and the accuracy the insufficient demand, the product innovation has been gradually regarded by the enterprise the creation value.

The 50 pieces yarn samples provided with Mean Time(明大) enterprise co., ltd.. The subjects were invited to measure their subjective impression of 50 different yarns using the Semantic Differential Method (SD). Composes the program to extract the Color Coherent Vector（CCV） as the color features of yarn images, LBP (local binary pattern), SCOV, SAC, VAR to analyze gray-scale of image as texture features, and to code the information of yarns’ weaves and the material. Take the color features, the texture features, the weave and the material’s code as the input layer, and the output layer is the value of 10 impression words. The Back-Propagation Networks（BPN）was trained to approximate the relationship between the kansei features and the features of yarns. The two ways are correlated with each other through a Neural Network mechanism, which is used to correlate the two feature spaces such that the retrieval system can enhance the designer to design the yarns efficiently and conveniently.

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