Neural Networks (NNs) are non-linear models and are widely used to model complex relationships, thus being well suited to formulate the product design process for matching design form elements to consumers’ affective preferences. In this paper, we construct 36 deep NN models, using one to four hidden layers with three different dropout ratios and three widely used rules for determining the number of neurons in the hidden layer(s). As a result of extensive experiments, the NN model using one hidden layer with 140 hidden neurons has the highest predicting accuracy rate (80%) and is used to help product designers determine the optimal form combination for new fragrance bottle design.

The compressed beamforming weights feedback is used in the precoded MIMO-OFDM system, e.g., IEEE 802.11n/ac WLAN, to reduce the amount of feedback information. The precoded sub-system at the receiver of this system needs to perform singular value decomposition (SVD) on the estimated channel matrices at all sub-carriers. The computed angles of the right singular vectors are to be feedback to the transmitter. Also the computed singular values and left singular vectors are to be used for designing the optimal detector. Under the exemplary scenario of four antennas at the access point and two antennas at the station, our designed architectures for the precoding sub-system can provide SVD throughput rates 16.8 and 50.4 M matrices/sec for the uplink and downlink transmissions, respectively.

Artificial neural networks (ANNs) have been applied successfully in a wide range of fields due to its effective learning ability. In this paper, we propose a grey relational analysis (GRA) based ANN model that can be used to build a design decision support database for facilitating the product design process and matching specific consumers' preferences. The result of an empirical application and a comparative study on fragrance bottle form design shows that the ANN models outperform the grey prediction models, indicating that the ANN technique is promising to help product designers design a new product that best meets consumers' needs.

In the traditional product design field, most studies focus on commercial products (e.g. furniture, vehicle, consumer electronics, and so on), while few studies pay attention on the craft art. As such, this paper conducts a consumer-oriented experiment on characteristic toys to determine how to best meet consumers' preferences for new product design. The characteristic toy is a good illustration to be an application of art and design, because it is not only one type of craft art, but also a commercial product. The characteristic toy has two features simultaneously of craft art and product design. In this study, the new approach proposed consists of two main methods, i.e. the quantification theory type I (QTTI) and the technique for order preference by similarity to ideal solution (TOPSIS). There are three QTTI models used to build a design decision support database. According to the design decision support database, a QTTI-based TOPSIS model is used to help product designers to obtain the optimal design alternatives that best meet consumers' preferences.