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Several important sensory physical quantities exist but are difficult to quantify, including food freshness. With the aid of a hyperspectral imaging system (HIS) and machine learning (ML), meat freshness is converted into a measurable physical quantity, i.e., freshness index (F. I.), in this study. F. I. is defined from meat fluorescence, which has a strong correlation with bacterial density, using a line-scan-type HIS stimulated at 365 nm. Combined with ML techniques, hyperspectral images are processed more efficiently. By employing linear discriminant and quadratic component analyses for hyperspectral images, F. I. can be estimated from its decision boundary after hyperspectral images are taken at an unknown freshness state. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life with the aid of home appliances and smartphones.

In this paper, a predictive direct power control (DPC) method based on a Kalman filter is presented for three-phase pulse-width modulation (PWM) rectifiers to improve the performance of rectifiers with source voltages that are distorted with harmonic components. This method can eliminate the most significant harmonic components of the source voltage using a Kalman filter algorithm. In the process of predicting the future real and reactive power to select an optimal voltage vector in the predictive DPC, the proposed method utilizes source voltages filtered by a Kalman filter, which can mitigate the adverse effects of distorted source voltages on control performance. As a result, the quality of the source currents synthesized using the PWM rectifier is improved, and the total harmonic distortion (THD) values are reduced, even under distorted source voltages.

A simplified model predictive control method is presented in this paper. This method is based on a future reference voltage vector for a three-phase four-leg voltage source inverter (VSI). Compared with the three-leg VSIs, the four-leg VSI increases the possible switching states from 8 to 16 owing to a fourth leg. Among the possible states, this should be considered in the model predictive control method for selecting an optimal state. The increased number of candidate switching states and the corresponding voltage vectors increase the calculation burden. The proposed technique can preselect 5 among the 16 possible voltage vectors produced by the three-phase four-leg voltage source inverters, based on the position of the future reference voltage vector. The discrete-time model of the future reference voltage vector is built to predict the future movement of the load currents, and its position is used to choose five preselected vectors at every sampling period. As a result, the proposed method can reduce calculation load by decreasing the candidate voltage vectors used in the cost function for the four-leg VSIs, while exhibiting the same performance as the conventional method. The effectiveness of the proposed method is demonstrated with simulation and experiment results.

For understanding consumer behaviors more closely to what would be observed in real-life, “situational” tests has been widely studied using two main approaches: ‘physical environment’ or ‘cognitive evocation’. Both approaches have shown advantages in adopting consumption context in consumer tests. However, it has not been investigated whether either one of the approaches may be more effective or whether using these approaches together would be more advantageous than using each solely in terms of bring in consumption context. The aims were to understand the effects of evocation in different environmental settings on consumers liking and to explore the differences of these effects based on consumer involvement levels. Consumers participated in one of the 4 contextual conditions of 2 × 2 factorial design, consisting of ‘evocation’ factor (with vs. without evocation instruction) and ‘environment’ factor (booth vs. simulated cafe), respectively. Consumers evaluated liking for two coffee samples and responded to a coffee involvement questionnaire. The results showed that both factors have influenced on consumer liking. These effects were different according to coffee types differing in hedonic levels. Vividness of evocation lasted longer in the simulated cafe setting, implying physical cues reinforcing cognitive evocation. When consumers were classified according to involvement scores, liking score of the high involvement group was little affected by either situational factors, while low involvement group was highly affected by the ‘environment’ factor. The findings of the study provide important perspectives to be considered for researches into “situational” tests, including which situational approach to apply as well as consumer involvement.

This paper proposes two model predictive current control (MPCC) methods utilizing two output voltages with variable application durations in one sampling period to control the output currents of single-phase voltage source inverters (VSIs). In the proposed methods, the application durations of the two voltages, as well as the selection of two output voltages used in the future sampling period, are determined by an optimization process to minimize current error inside the future sampling period and to eliminate current error at the end of the future sampling instant. By utilizing the two voltages with variable durations, the two proposed MPCC methods can reduce steady-state current errors and decrease output current ripples without increasing the sampling frequency in comparison with the conventional MPCC method, despite only three distinctive output voltages being allowed in the single-phase VSI. The two proposed methods along with the conventional MPCC method are compared in terms of current errors and total harmonic distortion. The effectiveness of the two proposed methods for the single-phase VSIs is verified with both simulation and experimental results.

The present work explored how consumers' product perceptions differ when flavor familiarity with the product set varied. Half of the samples used in this study contained fermented ingredients (fermented red pepper or gochujang, a traditional Korean fermented soybean/red pepper paste), and the others were top selling hot sauce products in the US market. Free-choice profiling was performed by Korean and US consumers and was analyzed using GPA. Descriptive analysis was conducted and analyzed using PCA. While Korean and US consumers perceived product similarly along the first principal dimension which described distinctive sensory differences among the products, in the next principal dimension, it was found that these consumers perceived the products differently. Observations indicated that this discrepancy seemed to be originated from differences in flavor familiarity. This study showed flavor familiarity not only influences one's preference but also may influence perception of foods such as hot sauces.