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A gaze control system for tracking Quasi-1D high-speed moving object is proposed, it can keep the object in the centre of the image within a certain range. Initially, the system structure is designed, and the tracking range of the system is expanded using a single saccade mirror. Then the model between the deflection angle of the saccade mirror and the pixel displacement is established. Finally, a frame-difference method based on image cropping is proposed to rapidly extract the moving object in the complex dynamic background. It feeds back the object position to the saccade mirror control system. The system adjusts the deflection angle of the saccade mirror in real time. Experimental results show that the system can satisfy the requirements of gaze control for tracking Quasi-1D high-speed moving object.

Physiological and metabolic profiles in tamarillo were investigated to reveal the molecular changes during fruit maturation. The firmness, ethylene production, soluble sugar contents, and metabolomic analysis were determined in tamarillo fruit at different maturity stages. The firmness of tamarillo fruit gradually decreased during fruit ripening with increasing fructose and glucose accumulation. The rapid increase in ethylene production was found in mature fruit. Based on the untargeted metabolomic analysis, we found that amino acids, phospholipids, monosaccharides, and vitamin-related metabolites were identified as being changed during ripening. The contents of malic acid and citric acid were significantly decreased in mature fruits. Metabolites involved in phenylpropanoid biosynthesis, phenylalanine metabolism, caffeine metabolism, monoterpenoid biosynthesis, and thiamine metabolism pathways showed high abundance in mature fruits. However, we also found that most of the mature-enhanced metabolites showed reduced abundance in over-mature fruits. These results reveal the molecular profiles during tamarillo fruit maturing and suggest tamarillos have potential benefits with high nutrition and health function.

Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal operation of modern machinery, especially under the varying operating conditions. In this paper, an end-to-end adaptive anti-noise neural network framework (AAnNet) is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures. The proposed AAnNet employs the random sampling strategy and enhanced convolutional neural networks with the exponential linear unit as the activation function to increase the adaptability of the neural network. Moreover, the gated recurrent neural networks with attention mechanism improvement are further adopted to learn and classify the features processed by the convolutional neural networks part. Besides, we try to explain how the network works by visualizing the intrinsic features of the proposed framework. And we explore the effect of the attention mechanism in the proposed framework. Experiments show that the proposed framework achieves state-of-the-art results on two datasets under varying operating conditions.

Due to global warming, high-temperature stress has become a major threat to plant growth and development, which causes a severe challenge to food security worldwide. Therefore, it is necessary to explore the plant bioactive molecules, which could be a promising approach to strengthening plant thermotolerance. Rosmarinic acid (RA) serves as a plant-derived phenolic compound and has beneficial and health-promoting effects for human beings. However, the involvement of RA in plant stress response and the underlying molecular mechanism was largely unknown. In this study, we found that exogenous RA application conferred improved thermotolerance in tomatoes. The transcript abundance and the enzyme activity of enzymatic antioxidants, such as ascorbate peroxidase (APX), catalase (CAT), glutathione reductase (GR), and dehydroascorbate reductase (DHAR), were further promoted by RA treatment in tomato plants subjected to high-temperature stress. Moreover, RA activated the antioxidant system and modulated the cellular redox homeostasis also associated with the redox status of nonenzymatic glutathione and ascorbic acid. The results of RNA-seq data showed that transcriptional regulation was involved in RA-mediated thermotolerance. Consistently, the gene expression of several high temperature-responsive transcription factors like HsfA2, and WRKY family genes were substantially induced by RA treatment, which potentially contributed to the induction of heat shock proteins (HSPs). Overall, these findings not only gave a direct link between RA and plant thermotolerance but also provided an attractive approach to protecting crop plants from high-temperature damage in a global warming future.

Fruits are excellent sources of essential vitamins and health-boosting minerals. Recently, regulation of fruit ripening by both internal and external cues for the improvement of fruit quality and shelf life has received considerable attention. Rosmarinic acid (RA) is a kind of natural plant-derived polyphenol, widely used in the drug therapy and food industry due to its distinct physiological functions. However, the role of RA in plant growth and development, especially at the postharvest period of fruits, remains largely unknown. Here, we demonstrated that postharvest RA treatment delayed the ripening in tomato fruits. Exogenous application of RA decreased ripening-associated ethylene production and inhibited the fruit color change from green to red based on the decline in lycopene accumulation. We also found that the degradation of sucrose and malic acid during ripening was significantly suppressed in RA-treated tomato fruits. The results of metabolite profiling showed that RA application promoted the accumulation of multiple amino acids in tomato fruits, such as aspartic acid, serine, tyrosine, and proline. Meanwhile, RA application also strengthened the antioxidant system by increasing both the activity of antioxidant enzymes and the contents of reduced forms of antioxidants. These findings not only unveiled a novel function of RA in fruit ripening, but also indicated an attractive strategy to manage and improve shelf life, flavor, and sensory evolution of tomato fruits.

Sparse component analysis (SCA) has been widely used for blind source separation(BSS) for many years. Recently, SCA has been applied to operational modal analysis (OMA), which is also known as output-only modal identification. This paper considers the sparsity of sources’ time-frequency (TF) representation and proposes a new TF-domain SCA under the OMA framework. First, the measurements from the sensors are transformed to the TF domain to get a sparse representation. Then, single-source-points (SSPs) are detected to better reveal the hyperlines which correspond to the columns of the mixing matrix. The K-hyperline clustering algorithm is used to identify the direction vectors of the hyperlines and then the mixing matrix is calculated. Finally, basis pursuit de-noising technique is used to recover the modal responses, from which the modal parameters are computed. The proposed method is valid even if the number of active modes exceed the number of sensors. Numerical simulation and experimental verification demonstrate the good performance of the proposed method.

The pitching axis of a satellite camera is controlled under the weightless environment. A novel active disturbance rejection controller is designed to eliminate the influences of the pitching axis. The novel active disturbance rejection controller is designed based on a new nonlinear function, and thus, this function is first established. The function exhibits better continuity and smoothness than previously available functions, hence, it can effectively improve the high-frequency flutter phenomenon. Therefore, the novel active disturbance rejection controller based on the new nonlinear function can eliminate disturbances of the pitching axis. The novel active disturbance rejection controller is composed of a tracking differentiator, a novel extended state observer, and a novel nonlinear state error feedback. The tracking differentiator is used to arrange the transient process. Nonlinear dynamics, model uncertainty, and external disturbances are extended to a new state. The novel extended state observer is utilized to observe this state. The overtime variation of the system can be predicted and compensated using the novel extended state observer. The novel nonlinear state error feedback is adopted to restrain the residual errors of the system. Finally, simulation experiments are performed, and results show that the novel active disturbance rejection controller exhibits better performance than the traditional active disturbance rejection controller.

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.