Application of autoencoders in cyber-empathic design

Abstract

A critical task in product design is mapping information from the consumer space to the design space. This process is largely dependent on the designer to identify and relate psychological and consumer level factors to engineered product attributes. In this way, current methodologies lack provision to test a designer's cognitive reasoning and may introduce bias through the mapping process. Prior work on Cyber-Empathic Design (CED) supports this mapping by relating user-product interaction data from embedded sensors to psychological constructs. To understand consumer perceptions, a network of psychological constructs is developed using Structural Equation Modeling for parameter estimation and hypothesis testing, making the framework falsifiable in nature. The focus of this technical brief is toward automating CED through unsupervised deep learning to extract features from raw data. Additionally, Partial Least Square Structural Equation Modeling is used with extracted sensor features as inputs. To demonstrate the effectiveness of the approach a case study involving sensor-integrated shoes compares three models -a survey-only model (no sensor data), the existing CED approach with manually extracted sensor features, and the proposed deep learning based CED approach. The deep learning based approach results in improved model fit.

Kısmi En Küçük Kareler Yapısal Eşitlik Modelinin Yeraltı Suyu Kalitesinin Değerlendirilmesinde Kullanımı

“…VT-YEM, temelde iki aşamada çalışır: birinci aşamada dışsal ölçme modeli ağırlıkları/yükleri hesaplanır, ikinci aşamada ise yapısal yol katsayıları ve R 2 hesaplanır. VT-YEM, alt çalışma aşamaları hesaba katılırsa toplam 7 adımda çalışır [16]: görünmese de ağırlık katsayısı 0.421 olarak bulunmuştur. Mg genel olarak yeraltı sularında Na + 'ya kıyasla sıralama yönünden daha az başat durumdadır [18], [19].…”

“…VT-YEM, temelde iki aşamada çalışır: birinci aşamada dışsal ölçme modeli ağırlıkları/yükleri hesaplanır, ikinci aşamada ise yapısal yol katsayıları ve R 2 hesaplanır. VT-YEM, alt çalışma aşamaları hesaba katılırsa toplam 7 adımda çalışır [16]:…”

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

“…The goal of an autoencoder is to preserve as much information as possible and also add new representations on top of the raw input data [39]. Some of the great applications of Autoencoders include dimensionality reduction [41], cyberemphatic design [42], molecular design [43] and many more.…”