Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.

The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R²_test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R²_test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

With the fish of crisped grass carp as the research object, this paper analyzes the hardness-hyperspectral data of crisped grass carp with a texture analyzer and a visible - near infrared hyperspectral imaging system (400-1100nm), and then proposes a method to quickly detect the hardness of crisped grass carp. In this study, the hardness of 15 fish crisped grass carp was analyzed with a texture analyzer, and it was found that there was a difference in hardness between different fish with a significance P<0.05.Four spectral bands affecting the hardness of crisped grass carp were found by using the random forest algorithm, which indicated that there was a connection between hardness and hyperspectrum. Four pretreatment methods were used, among which SNV pretreatment method had the best effect, R²was 0.80. PLS, SVM and BP-NN prediction models were established respectively, and R²and RMSEP of the models were used as evaluation indexes to compare the advantages and disadvantages. The results are as follows: R²is 0.8033, 0.8564, 0.8814 and RMSEP are 3.6417, 2.6891 and 2.6128 respectively, among which BP-NN prediction model R²is 0.8814 and RMSEP is 2.6128, which has the most significant effect. Therefore, BP-NN prediction model is more suitable for nondestructive testing of crisped grass carp hardness.

Deploying pigeon age detection model on edge equipment can solve the problem of video transmission delay and reduce the pressure of network transmission. Based on the deployment of edge devices, we made some improvements to You Only Look Once version 5 (YOLOv5) to form a new, lightweight, high-performance detector named LN-STEP-YOLO. In order to reduce the size of the model, we halved the number of channels in the YOLOv5s model. However, the decrease in the number of channels brings some problems, such as low global information acquisition, retention of image redundancy information, attention deficit, etc. To solve these problems, we did the following work. First, we proposed a new convolution structure with the effect of a large convolution kernel, StepConv. Second, a 2 × 2 convolution with step size 2 was used at the input to split each image into individual patches. Third, External Attention (EA) was introduced in the bottleneck structure. Fourth, a modified extremely separated convolutional block (XsepConv) was used for downsampling. Finally, we replaced the batch normalization (BN) of all non-downsampled layers with layer normalization (LN). The results showed that the improved algorithm outperformed generic lightweight networks such as Mixnet, Mobilenetv3, and Ghostnet to distinguish small-sized, overlapping pigeons, achieving 92.8% mean average precision (mAP) at about 17% of YOLOv5s parameters, 0.1% lower than that achieved by use of YOLOv5s. In addition, the improved method had 3.7G floating point operations (FLOPs) and 1.25G parameters, which allowed the detection of the growth stages of pigeons in real environments and provided a reference to guide placement of feeders in automated pigeon farming.