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A non-iterative method is presented to accurately extract the five parameters of single diode model of solar cells in this paper. This method overcomes the problems of complexity and accuracy by simplifying the calculation process. Key parts of the equation are to be adjusted dynamically so that the desired five parameters can be obtained from the I-V curve. Then, the I-V and P-V characteristic curves of solar cells are used to compare the effectiveness of this method with other methods. Furthermore, the root mean square error analysis shows that this method is more applicable than other methods. Finally, the I-V and P-V characteristics simulated by using the extracted parameters in this method are compared and discussed with the experimental data of solar cells under different conditions. In fact, this extraction process can be regarded as an effective and accurate method to estimate solar cells’ single diode model parameters.

An analytical surface-potential-based drain current model of symmetric double-gate (sDG) MOSFETs is described as a SPICE compatible model in this paper. The continuous surface and central potentials from the accumulation to the strong inversion regions are solved from the 1-D Poisson’s equation in sDG MOSFETs. Furthermore, the drain current is derived from the charge sheet model as a function of the surface potential. Over a wide range of terminal voltages, doping concentrations, and device geometries, the surface potential calculation scheme and drain current model are verified by solving the 1-D Poisson’s equation based on the least square method and using the Silvaco Atlas simulation results and experimental data, respectively. Such a model can be adopted as a useful platform to develop the circuit simulator and provide the clear understanding of sDG MOSFET device physics.

We propose an improved lumped-parameter equivalent circuit model to describe S-shaped I⁻V kinks observed from organic solar cells. Firstly, to predict the S-shaped I⁻V kinks accurately in both the first and fourth quadrants, a shunt resistor in parallel with extraction diode is added to our previous model. Secondly, based on the Newton⁻Raphson method, we derive a solution to our improved circuit. Thirdly, our solution is verified by the method of least squares and experiments. Finally, compared with our previous work, the improved circuit has higher accuracy in demonstrating S-shaped I⁻V kinks in the first and fourth quadrants. Such an improved model is suitable for circuit simulations of organic solar cells.

An improved lumped-parameter equivalent circuit model is proposed to describe S-shaped I–V characteristics of organic solar cells (OSCs). This model originates but differs from Mazhari’s model. As a minor but important modification, a shunt resistance is added to Mazhari’s model to increase the accuracy of simulating the S-shaped kink in the third quadrant. Subsequently, we present a terminal current-voltage equation set and derive an analytical solution to the improved model. Furthermore, we verify the analytical solution to our model by using the least square method and validate our model by using the experimental I–V curves examined from OSCs. Compared with Mazhari’s model, our model has greater accuracy in interpreting the S-shaped kink with linear-like rise in the third quadrant. As a result, our improved model is suitable to explain the S-shaped I–V characteristics of organic solar cells in the whole operational region, especially for the S-shaped kink in the third quadrant.

Sheep facial expressions are valuable indicators of their pain levels, playing a critical role in monitoring their health and welfare. In response to challenges such as missed detections, false positives, and low recognition accuracy in sheep facial expression recognition, this paper introduces an enhanced algorithm based on YOLOv8n, referred to as SimAM-MobileViTAttention-EfficiCIoU-AA2_SPPF-YOLOv8n (SMEA-YOLOv8n). Firstly, the proposed method integrates the parameter-free Similarity-Aware Attention Mechanism (SimAM) and MobileViTAttention modules into the CSP Bottleneck with 2 Convolutions(C2f) module of the neck network, aiming to enhance the model’s feature representation and fusion capabilities in complex environments while mitigating the interference of irrelevant background features. Additionally, the EfficiCIoU loss function replaces the original Complete IoU(CIoU) loss function, thereby improving bounding box localization accuracy and accelerating model convergence. Furthermore, the Spatial Pyramid Pooling-Fast (SPPF) module in the backbone network is refined with the addition of two global average pooling layers, strengthening the extraction of sheep facial expression features and bolstering the model’s core feature fusion capacity. Experimental results reveal that the proposed method achieves a mAP@0.5 of 92.5%, a Recall of 91%, a Precision of 86%, and an F1-score of 88.0%, reflecting improvements of 4.5%, 9.1%, 2.8%, and 6.0%, respectively, compared to the baseline model. Notably, the mAP@0.5 for normal and abnormal sheep facial expressions increased by 3.7% and 5.3%, respectively, demonstrating the method’s effectiveness in enhancing recognition accuracy under complex environmental conditions.

Abstract In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective.

Researching the thermal characteristic parameters of alfalfa is of fundamental importance for accurately measuring heat transfer and distribution during the compression process. Therefore, the thermal characteristic parameters were measured using the transient plane heat source method. Additionally, the study examined the impact of moisture content and particle size on the thermal characteristic parameters of alfalfa. The experimental results indicated that the thermal conductivity of alfalfa increased with higher moisture content, and it decreased with the increase of particle size. Similarly, the specific heat capacity increased with higher water content, while the particle size had little effect on specific heat capacity. The thermal diffusion coefficient initially decreased and then stabilized with higher water content. Moreover, the influence of particle size on thermal diffusion coefficient was not significant. The obtained thermal characteristic parameters are valuable for investigating temperature changes during the densification process of alfalfa.

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

Accurate identification of individual animals plays a pivotal role in enhancing animal welfare and optimising farm production. Although Radio Frequency Identification technology has been widely applied in animal identification, this method still exhibits several limitations that make it difficult to meet current practical application requirements. In this study, we proposed ViT-Sheep, a sheep face recognition model based on the Vision Transformer (ViT) architecture, to facilitate precise animal management and enhance livestock welfare. Compared to Convolutional Neural Network (CNN), ViT is renowned for its competitive performance. The experimental procedure of this study consisted of three main steps. Firstly, we collected face images of 160 experimental sheep to construct the sheep face image dataset. Secondly, we developed two sets of sheep face recognition models based on CNN and ViT, respectively. To enhance the ability to learn sheep face biological features, we proposed targeted improvement strategies for the sheep face recognition model. Specifically, we introduced the LayerScale module into the encoder of the ViT-Base-16 model and employed transfer learning to improve recognition accuracy. Finally, we compared the training results of different recognition models and the ViT-Sheep model. The results demonstrated that our proposed method achieved the highest performance on the sheep face image dataset, with a recognition accuracy of 97.9%. This study demonstrates that ViT can successfully achieve sheep face recognition tasks with good robustness. Furthermore, the findings of this research will promote the practical application of artificial intelligence animal recognition technology in sheep production.

We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

To quickly realize facial identity recognition in sheep, this paper proposes a lightweight detection algorithm based on SSD with a self-constructed dataset. Firstly, the algorithm replaces the VGG16 backbone of SSD with the lightweight neural network SqueezeNet, creating a lightweight hybrid network model. Secondly, an ECA mechanism is introduced at the front end of the pooling layer with a parameter volume of 12 × 1000 into the feature extraction network. Lastly, the smoothL1 loss function is replaced with the BalancedL1 loss function. The optimal model size has been reduced from the original SSD’s 132 MB to 35.8 MB. The average precision is 82.39%, and the mean frame rate is 66.11 frames per second. Compared to the baseline SSD model, the average precision has improved by 2.17%, the model volume has decreased by 96.2 MB, and the detection speed has increased by 7.13 frames per second. Using the same dataset on different target detection models for comparison tests, the average accuracy mean values are improved by 2.17%, 3.63%, and 1.30% compared to the SSD model, Faster R-CNN model, and Retinanet model, respectively, which substantiates a better overall performance compared to the pre-improvement model. This paper proposes an improved model that significantly reduces the model size and its computation while keeping the model performance at a high level, providing a methodological reference for the digitization of livestock farming.

Large-scale sheep farming has conventionally relied on barcodes and ear tags, devices that can be difficult to implement and maintain, for sheep identification and tracking. Biological data have also been used for tracking in recent years but have not been widely adopted due to the difficulty and high costs of data collection. To address these issues, a noncontact facial recognition technique is proposed in this study, in which training data were acquired in natural conditions using a series of video cameras, as Dupo sheep walked freely through a gate. A key frame extraction algorithm was then applied to automatically generate sheep face data sets representing various poses. An improved MobilenetV2 framework, termed Order-MobilenetV2 (O-MobilenetV2), was developed from an existing advanced convolutional neural network and used to improve the performance of feature extraction. In addition, O-MobilenetV2 includes a unique conv3x3 deep convolution module, which facilitated higher accuracy while reducing the number of required calculations by approximately two-thirds. A series of validation tests were performed in which the algorithm identified individual sheep using facial features, with the proposed model achieving the highest accuracy (95.88%) among comparable algorithms. In addition to high accuracy and low processing times, this approach does not require significant data pre-processing, which is common among other models and prohibitive for large sheep populations. This combination of simple operation, low equipment costs, and high robustness to variable sheep postures and environmental conditions makes our proposed technique a viable new strategy for sheep facial recognition and tracking.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study developed a large-scale benchmark dataset, Sheepface-107, comprising 5350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol was developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the rest area and the drinking area, the kernel density analysis method was used to cluster the trajectory point data. At the same time, the vegetation index of the experimental area, including elevation, slope and aspect data, was obtained through satellite remote sensing images. Therefore, using trajectory data and remote sensing image data to establish a neural network model of grazing intensity of sheep, the accuracy of the model could be high. The results showed that the best input parameters of the model were the combination of vegetation index, sheep weight, duration, moving distance and ambient temperature, where the coefficient of determination R2=0.97, and the mean square error MSE = 0.73. The error of grazing intensity obtained by the model is the smallest, and the spatial-temporal distribution of grazing intensity can reflect the actual situation of grazing intensity in different locations. Monitoring the grazing behavior of sheep in real time and obtaining the spatial-temporal distribution of their grazing intensity can provide a basis for scientific grazing.

A high update rate is always one of the vital indices of star trackers. By recording star positions at N moments within a single star image, the multi-exposure imaging approach (MEIA) proposed in an earlier study can improve the attitude update rate of star trackers by N times. Unfortunately, when the existing star matching method is adopted to match the observed and predicted stars in MEIA, the matching time is significantly increased with the increase in multi-exposure times, N, or the number of navigation stars, M, which sharply affects the MEIA’s performance. Therefore, a fast star matching method based on double K-vector lookup tables (DKVLUTs) is proposed to address the above issue. In this method, the information of all predicted stars is used to establish the DKVLUT, and thus, the speed of the whole matching process between observed and predicted stars would be increased effectively by means of the DKVLUT. Both simulations and experiments are conducted to verify the performance of the proposed method. The results both show that the matching time of the proposed method is reduced by nearly one order of magnitude compared with that of the existing method, which demonstrates the feasibility and effectiveness of the proposed method.

An attempt to introduce assistive vibration into the process of biomass briquetting was carried out, with a focus on lowering the energy requirement and improving product quality. The effects of assistive vibration on the surface morphology of briquettes using corn stalk and wheat straw as the experimental materials was investigated, and it was found that assistive vibration can increase the flow capacity of the material and improve the press transmission as well as the uniformity of internal stresses to facilitate the inner-layer-material compression and lower the springback of the compressed material. The biomass particles were still bonded primarily by mechanical interlocking and solid bridges, but the distribution range and size of the voids or gaps between adjacent particles were reduced, and the particles or fibers of thicker layer appeared to be “lying down” instead of “standing,” indicating a higher density of the product compared with the case of compaction without vibration assistance.

The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management.