Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.

In this paper, the fault diagnosis problem for power converters is considered. Given that the existing fault diagnosis models rarely address the problems of the data noise and the new faults that are never emerged in the database, thus, an optimized fault diagnosis model for power converters based on temporal convolutional network (TCN) is proposed. Our contributions include: 1) unknown faults can be efficiently distinguished with an optimized classifier; 2) the proposed model has good robustness and reliability under noisy environment without any subsidiary pre-denoising algorithm; 3) it can realize adaptive feature extraction, and the parameters are small. Experimental results on a three-phase voltage inverter platform demonstrate that the proposed approach is efficient and can be adaptively applied to various real applications.

High quality online uninterruptible power supplies (UPS) are widely used in applications e.g. precision medical equipment with critical loads. This paper proposes a low-cost and high performance online UPS system. Firstly, the performance improvement of a bidirectional switch based bridgeless power factor (PFC) converter, which is regulated by a hybrid control strategy with load current feedforward is studied. Secondly, the proposed closed-loop controller integrates the improved predictive current control with predictive voltage control and thereby it applies to the second-stage neutral-point-clamped (NPC) three-level inverter to reduce harmonics of the output ac voltage. Moreover, a push-pull DC-DC converter to boost the voltage as the backup energy source interfaces a 24V battery bank. Considering the complexity of the UPS system as well as cost reduction, we implement a multi-task decomposition method for control on a monolithic low-cost digital signal processor (DSP) embedded with Control Law Accelerator (CLA). Finally, the experimental results from a 1kVA a prototype can effectively verify validly of the theoretical analysis and design.

Potential malicious attacks have been a significant security concern for network system applications. However, there are few studies on filtering for hybrid network attacks in switching systems. This paper considers a Kalman filtering problem for the switched systems that suffer from deception attacks and denial-of-service attacks. A new network transmission model for switching systems is established. Then, based on the minimum mean square error criterion, a Kalman filter with low conservatism is designed for the discrete-time switched system. The newly switched Kalman gain matrix is deduced including the random variation of the switching signal after being attacked by the network. Finally, the effectiveness of the proposed filter is verified by the numerical simulation. Note to Practitioners —A switching system is considered to be a typical hybrid system. As it often works in a network environment, the switching signal is vulnerable to the network and thus to various cyber-attacks (e.g. spoofing attacks and denial-of-service attacks). Few studies have been conducted on the impact of switching signals in network transmission. To address this problem, this paper proposes a Kalman filter design method with low conservativeness, which describes network attacks and data loss by building a Kalman filter model associated with the switching signal in terms of satisfying Bernoulli random variables. For practitioners, the ability to recover data to a certain extent when the data transmission is affected by different network attacks will save a lot of costs and is important for improving the stability and performance of switching systems. Our future work will aim to improve the performance of different filtering algorithms under different cyber-attacks.

Cross‐regulation is one of the critical challenges in single‐input and multi‐output converters. In this study, new design guidelines and an analytical model for studying the cross‐regulation between the dual‐outputs of the fly‐buck converter are proposed. A small‐signal AC model is first established to obtain control over the output and to determine the cross‐influence transfer functions of the system. Then, a load regulation coefficient and method for analysing the cross‐regulation while considering the load magnitude and load change are proposed to study the characteristics of the fly‐buck converter. Finally, to demonstrate the rationality behind the influencing factors over cross‐regulation based on the resulting characteristics, a fly‐buck converter with double control variables and lower cross‐regulation is designed. Simulations and experimental prototypes are implemented to demonstrate the effectiveness of the proposed theoretical analysis.