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This paper investigates the attitude control of rigid spacecraft in the presence of uncertainties, disturbances, and actuator faults. In order to effectively address these challenges and improve the performance of the system, a novel actor-critic neural-network-based fractional-order sliding mode control (ACNNFOSMC) has been developed for spacecraft. The integration of actor-critic neural network, fractional-order theory, and sliding mode control enables dual functionality: the actor-critic neural network serves to approximate the aggregate of uncertain parameters, disturbances, and actuator faults, thereby facilitating their compensation, while the fractional-order sliding mode control mechanism significantly improves the system’s tracking precision and overall robustness against uncertainties. Theoretical analyses are presented to analyze the stability of the proposed control framework. Thorough examination via simulation experiments affirms the effectiveness and control precision of attitude of our proposed control strategy, even in complex operational scenarios.

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

Carbon fiber has excellent mechanical properties and plays an important role in modern industry. However, due to the complexity of the carbon fiber widening process, the industrial application of carbon fiber is limited. By designing the carbon fiber widening equipment of automaton, the relationship between the widening width of carbon fiber and the process parameters is studied, and the optimum developing process parameters are obtained, to improve the performance of carbon fiber composites to a certain extent. In this study, the widening process of carbon fiber was studied based on the mechanical broadening method. Firstly, an automatic broadening equipment was designed, and the effects of the initial tension, the number of straight rods, the number of convex rods, and the drawing speed on the widened width during the broadening process were discussed. The widening effect was evaluated by SEM imaging and mechanical testing. At the same time, the factors affecting the broadening width and broadening defects during the broadening process were analyzed, and the optimal broadening process parameters were obtained. The results showed that within a specific range, a higher initial tension, a greater number of convex rods, and an appropriate speed resulted in relatively smaller damage to the broadening of carbon fibers. Through the design of automatic broadening, this experiment explores optimal broadening process parameters, provides a reference for the improvement of the carbon fiber broadening process and further promotes large-scale industrial applications of carbon fiber.

The crossbeam is frequently subjected to alternating loads during work as an essential load-bearing part of the crane. However, due to the large volume and the limitations of detection technology, it is impossible to realize online monitoring of the mechanical state. The ongoing advancement of ROMing and digital twin technology plays a pivotal role in facilitating the resolution of this particular issue. In this paper, we take the crane beam as the physical entity and combine the Twin Builder reduced-order technology and Deployer digital twin deployment technology to establish a digital twin of the beam. The load recognition model within the twin system exhibits a prediction error rate of ±5%. Furthermore, the accuracy of the ROM surpasses that of conventional machine learning models by a factor of 25. Upon deployment on the web platform, the results are delivered within 0.5 s, representing a substantial improvement as it is merely 1/15 of the time required for traditional 3D displays. The digital twin online monitoring system has the advantages of high accuracy and low requirements for monitoring equipment, which can be widely used in engineering practice to solve the problem that the mechanical state of large parts cannot be accurately monitored online.

There will be great damage in the process of harvesting, transporting, and storing after grain matures. The injury rate is as high as 8% to 12%. After damage, the germination rate of the grain becomes lower, the quality decreases, and it is easily infected with pests and molds. This study of the grain-crushing characteristics is of great significance to ensure grain quality, and an accurate crushing model is a prerequisite for effectively simulating crushing characteristics. This paper studies the shattering characteristics of wheat grains. Two-dimensional slices of wheat grain were obtained using X-ray tomography technology. Then, an accurate three-dimensional outer contour model of the wheat particle was constructed using image filtering and segmentation algorithms. The particle filling process was conducted using EDEM 2018 software to establish a wheat particle simulation model based on the Hertz–Mindlin with a Bonding contact model. Using the DOE experimental design method, single-factor experiments, Plackett–Burman experiments, steepest-climb experiments, and Box–Behnken were designed to study the fragmentation characteristics of wheat particles combined with parameter calibration and physical experiments. The test results show that the normal stiffness per unit area is 7.392 × 1010 N/m3, critical normal stress is 5.293 × 106 Pa, critical tangential stress is 5.001 × 106 Pa, and the relative error about 3%, which verifies the reliability of the simulation parameters in the discrete-element crushing model of wheat grain. This study focuses on two essential aspects: 1. establishing an accurate wheat-grain contour model; and 2. calibrating the bonding parameters of the discrete-element simulation model of wheat grain. The wheat grain discrete-element crushing model and the calibration of its bonding parameters are constructed to provide a foundation for the study of wheat-grain crushing characteristics. It is of great significance to study the situation of wheat grains and where cracks are produced. In this paper, an accurate model of the wheat-grain contour is established, and the bonding parameters of the discrete-element simulation model of the wheat grain are calibrated. The calibration of the model of the discrete elements of wheat-grain fragmentation and its bonding parameters will provide a basis for studying the crushing characteristics of wheat grain. Understanding the condition of wheat grains and the causes of cracks carries significant academic significance.

The bridge-type bridge crane is a common lifting equipment used in modern factories and workshops. During the crane’s operation, the positioning of the trolley and the swing of the load can significantly impact the bridge crane’s safety and reliability. In this paper, we propose a variable universe fuzzy multi-parameter self-tuning PID (VUFMS-PID) control strategy for controlling the trolley’s movement. Our control strategy uses scaling factor variation to dynamically adjust the number of fuzzy control rules based on the system error and error rate of change. This approach improves control accuracy and enhances the crane’s stability and safety. Simulation results demonstrate that our proposed control strategy outperforms both the fuzzy PID and traditional PID control strategies. Specifically, it reduces the crane trolley’s positioning time and the maximum swing angle of the load. Our control strategy exhibits good adaptive ability and robustness, which further improves the stability and safety of the bridge-type bridge crane operation.

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.