Real‐time displacement measurement for long‐span bridges using a compact vision‐based system with speed‐optimized template matching

This paper introduces a new accelerating algorithm, efficient match slimmer (EMS), specifically designed to lighten computational loads of sophisticated template matching algorithms, enabling these algorithms to be effectively run on single-board computers. Utilizing EMS in conjunction with a robust template matching algorithm, we have developed Raspberry Vision—a compact, cost-effective, and real-time vision-based system. Its compactness and portability facilitate a practical measurement strategy that not only minimizes the camera-to-target distance but also simplifies the camera calibration process in bridge displacement monitoring, thereby enhancing measurement accuracy. The performance of the system is estimated on two operational suspension bridges. The results demonstrate that Raspberry Vision, equipped with the measurement strategy, can significantly improve the measurement accuracy in the long-span bridge test and is also suitable for cross-sea bridge measurements

Synchronization of the duffing oscillator by using terminal sliding mode control

This paper presents a terminal sliding mode controller used for the chaos synchronization of the Duffing oscillator. The error dynamical equation between the master subsystem and the slave subsystem is established firstly. Then the controller drives the error variables of the error dynamics to zero. The time taken to reach the equilibrium point from any initial states is guaranteed to be in finite time and the chaos synchronization is realized by using the controller. Simulation results are presented to validate the analysis

A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables

Accurate and timely crop yield estimation is crucial for crop market planning and food security. Combining remotely sensed big data with deep learning for yield estimation has attracted extensive attention. However, it is still challenging to understand and quantify the time cumulative effects of crop growth over time for crop yield estimation. In this study, we combined the powerful feature extraction capability of the convolutional neural network (CNN) and the advantage of time series memory of the gated recurrent unit (GRU) network to develop a novel deep learning model called CNN-GRU for estimating county-level winter wheat yields in the Guanzhong Plain using three remotely sensed variables, vegetation temperature condition index (VTCI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR). The CNN-GRU model was able to extract features related to yield from the input variables and the accuracy of the proposed model (R2 = 0.64, RMSE = 462.56 kg/ha, MRE = 8.90 %) was higher than that of the GRU model (R2 = 0.62, RMSE = 479.79 kg/ha, MRE = 9.34 %), and the CNN-GRU model's reliability and robustness were confirmed by applying the leave-one-year-out cross-validation. Furthermore, we applied the proposed CNN-GRU model to simulate the wheat yields in the Plain pixel by pixel and examined the spatiotemporal patterns of the estimated yields. The distribution of yields presented the spatial characteristics of low yields in the east and high yields in the west, and the inter-annual variation characteristics of overall stability and steady increase. Additionally, we explored the possibility of timely prediction of winter wheat yield and the contribution of the multi-variables at different growth stages to yield estimation based on the ability of deep learning to reveal cumulative effects and non-linear relationships between influencing factors and yield. It was found that the information reflected by the multi-variables from late March to late April was important for yield estimation and the best prediction could be achieved approximately 20 days before the harvest of winter wheat. Our study demonstrated that combining CNN and GRU was an efficient and promising approach to improve yield estimation, offering great promise for global crop yield estimation

Natural cracks inspection data for multiphysics electromagnetic pulsed thermography

Emerging integrated techniques, sensing and monitoring of material degradation and cracks are increasingly required for characterising the structural integrity and safety of infrastructure. This data set includes the simulation and experimental test of natural cracks inspection using multiphysics electromagnetic pulsed thermography. This technique enables the interpretation of multiple physical phenomena i.e. magnetic flux leakage, induced eddy current and induction heating linking to physics as well as signal processing algorithms to provide abundant information of material properties and defects. New features are proposed using 1st derivation that reflects multiphysics spatial and temporal behaviors to enhance the detection of crack orientations. Promising results robust to lift-off changes and invariant features for artificial and natural crack detection have demonstrated that the proposed method significantly improves defect detectability. It opens up multiphysics sensing and integrated NDE with potential impact for natural understanding and better quantitative evaluation of natural cracks including stress corrosion crack (SCC) and rolling contact fatigue (RCF)

Natural bioactive peptides to beat exercise-induced fatigue: A review

Exercise-induced fatigue is charactered by the feeling of tiredness and a decrease in muscle performance resulting from intense and prolonged exercise. With the development of modern society, exercise-induced fatigue has become a widespread problem besetting people's daily life. Over the years, increasing attention has been paid to the study of anti-fatigue peptides. Several animal models have been developed to mimic exercise-induced fatigue, which could be employed to measure the activities of anti-fatigue peptides isolated from a wide range of sources. A number of natural bioactive peptides were identified with ability to prevent and alleviate exercise-induced fatigue via various complex biological reactions, with possible molecular mechanisms being also explored extensively. In this review, we summarize the major research findings on anti-fatigue peptides, including the isolation and preparation of anti-fatigue peptides, the widely adopted methods for evaluation of anti-fatigue activities, and possible anti-fatigue mechanisms. Current evidence strongly supports that anti-fatigue peptides may relieve exercise-induced fatigue via multiple mechanisms, including participation and regulation of energy metabolism; inhibition of inflammatory responses; reduction of reactive oxygen species content; and regulation of neurotransmitters, etc. In conclusion, the review provides key research perspectives to inform further research on anti-fatigue peptides for the food industry

A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables

Accurate and timely crop yield estimation is crucial for crop market planning and food security. Combining remotely sensed big data with deep learning for yield estimation has attracted extensive attention. However, it is still challenging to understand and quantify the time cumulative effects of crop growth over time for crop yield estimation. In this study, we combined the powerful feature extraction capability of the convolutional neural network (CNN) and the advantage of time series memory of the gated recurrent unit (GRU) network to develop a novel deep learning model called CNN-GRU for estimating county-level winter wheat yields in the Guanzhong Plain using three remotely sensed variables, vegetation temperature condition index (VTCI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR). The CNN-GRU model was able to extract features related to yield from the input variables and the accuracy of the proposed model (R2 = 0.64, RMSE = 462.56 kg/ha, MRE = 8.90 %) was higher than that of the GRU model (R2 = 0.62, RMSE = 479.79 kg/ha, MRE = 9.34 %), and the CNN-GRU model's reliability and robustness were confirmed by applying the leave-one-year-out cross-validation. Furthermore, we applied the proposed CNN-GRU model to simulate the wheat yields in the Plain pixel by pixel and examined the spatiotemporal patterns of the estimated yields. The distribution of yields presented the spatial characteristics of low yields in the east and high yields in the west, and the inter-annual variation characteristics of overall stability and steady increase. Additionally, we explored the possibility of timely prediction of winter wheat yield and the contribution of the multi-variables at different growth stages to yield estimation based on the ability of deep learning to reveal cumulative effects and non-linear relationships between influencing factors and yield. It was found that the information reflected by the multi-variables from late March to late April was important for yield estimation and the best prediction could be achieved approximately 20 days before the harvest of winter wheat. Our study demonstrated that combining CNN and GRU was an efficient and promising approach to improve yield estimation, offering great promise for global crop yield estimation

Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning

Enhanced Third-Order Optical Nonlinearity of Flexibly Synthesized h-BN Film Via Localized Laser Oxidation

Hexagonal boron nitride film was achieved using ball-milled exfoliation and functionalization method for high concentration solution. Enhanced third-order nonlinearity was investigated during interaction with high intensity laser source owing to the formation of oxidization group

Natural bioactive peptides to beat exercise-induced fatigue: A review

Exercise-induced fatigue is charactered by the feeling of tiredness and a decrease in muscle performance resulting from intense and prolonged exercise. With the development of modern society, exercise-induced fatigue has become a widespread problem besetting people's daily life. Over the years, increasing attention has been paid to the study of anti-fatigue peptides. Several animal models have been developed to mimic exercise-induced fatigue, which could be employed to measure the activities of anti-fatigue peptides isolated from a wide range of sources. A number of natural bioactive peptides were identified with ability to prevent and alleviate exercise-induced fatigue via various complex biological reactions, with possible molecular mechanisms being also explored extensively. In this review, we summarize the major research findings on anti-fatigue peptides, including the isolation and preparation of anti-fatigue peptides, the widely adopted methods for evaluation of anti-fatigue activities, and possible anti-fatigue mechanisms. Current evidence strongly supports that anti-fatigue peptides may relieve exercise-induced fatigue via multiple mechanisms, including participation and regulation of energy metabolism; inhibition of inflammatory responses; reduction of reactive oxygen species content; and regulation of neurotransmitters, etc. In conclusion, the review provides key research perspectives to inform further research on anti-fatigue peptides for the food industry