No abstract

Conventional deadbeat predictive current control (DBPCC) based on space vector modulation (SVM) shows quick dynamic responses and a good steady‐state performance in induction motor (IM) drives. However, the motor parameters change during operation due to temperature and saturation changes, leading to inaccurate reference voltage vectors and a degraded performance. Furthermore, conventional DBPCC uses a fixed vector sequence of 0127 over the entire speed range, which results in large current harmonics at high modulation indices. To address the above issues, this paper proposes a robust deadbeat predictive current control (RDBPCC) for IM drives. Based on an ultra‐local model, the proposed method updates the input voltage gain and unknown system components online according to the voltage and current of the previous two control cycles. Because the final control expression contains only the measured stator current and voltage values, the model shows a strong robustness. The steady‐state performance is significantly improved by selecting the optimal vector sequence according to the modulation index based on the principle of current harmonic minimization. The experimental results confirm that, compared with conventional vector control and conventional DBPCC, the proposed method achieves a strong parameter robustness and reduces the current total harmonic distortion (THD) by more than 10% and 20% at high‐modulation indices.

Photo-oxidation is one of the main causes of oxidative deterioration in food. Understanding of the mechanisms of photo-oxidation is essential for food science students when pursuing careers in food safety and quality assurance. However, few food chemistry laboratories emphasize photo-oxidation. Thus, we develop a laboratory activity that trains students to evaluate the level of photo-oxidation by monitoring the early (photosensitizer), middle (dissolved oxygen), and late [thiobarbituric acid reactive substances (TBARS) value] indicators of photo-oxidation in a full-fat milk model under two types of light spectra (fluorescent and LED light). Through data analysis and post-laboratory discussion, students well understood the mechanism of photo-oxidation and the effect of light wavelength on the rate of reaction in food products by characterizing the oxidation kinetics at different stages of oxidation. This laboratory was designed for upper-level undergraduate students of food science and technology majors (n = 30), the learning outcome shows that students acquire practical skills in operating instruments that they may encounter in food industry as well as quantitative analytical methods that are used to estimate the shelf life of food products.

Machine learning (ML) featured on its ability of learning and extracting features from a large set of data and automatically building statistical models. Through cooperation with intelligent sensors, which is designed to imitate human organs to analyze the sensory characteristics of foods, ML-based intelligent sensory systems such as electronic nose (E-nose) and electronic tongue (E-tongue) are developed for sensing applications in food industry. Consumption of alcohol beverages keep growing worldwide in recent years and fraudulent activities are stimulated due to the high price of alcoholic drinks, which motivates the application of intelligent sensory technology with high efficiency and accuracy for real-time quality control. Thus, this paper firstly summarizes the novel intelligent sensors that is suitable for sensory evaluation and the advanced ML algorithms used to create intelligent systems. Then the paper describes the mechanism of commercial ML-enabled intelligent devices and summarizes their practical sensing applications on the real-time quality control of a variety of alcoholic beverages, in term of detection of frauds and adulterations, aroma analysis, monitoring of the production process, and correlation with human sensory perception. Finally, the potential applications and future opportunities of ML-enabled intelligent sensor systems in the alcohol industry are discussed.