Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like the product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect

Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect.Comment: 11 pages, 10 figures, peer-reviewed at the KEER 2022 conferenc

Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.

Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.

Residual Force Method for damage identification in a laminated composite plate with different boundary conditions

The strongest point about damage identification based on the dynamic measurements, is the ability perform structural health evaluation globally. Researchers in the last few years payed more attention to damage indicators based on modal analysis using either frequencies, mode shapes, or Frequency Response Functions (FRFs). This paper presents a new application of damage identification in a cross-ply (0°/90°/0°) laminated composite plate based on Force Residual Method (FRM) damage indicator. Considering single and multiple damages with different damage levels. As well as investigating the SSSS and CCCC boundary conditions effect on the estimation accuracy. Moreover, a white Gaussian noise is introduced to test the challenge the technique. The results show that the suggested FRM indicator provides accurate results under different boundary conditions. Favouring the SSSS boundary condition than the CCCC for 3% noise

Damage identification in steel plate using FRF and inverse analysis

Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA).  The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance.  Both in convergence speed and CPU time

Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems

Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm

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